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Video game live streaming and the perception of female gamers
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Video game live streaming and the perception of female gamers
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Content
Video Game Live Streaming and the Perception of Female Gamers
by
Lena Uszkoreit
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
In Communication
Conferred on August 8th, 2017
II
Acknowledgements
This study would not have been possible without the two women who produced the
content I assigned my participants to watch. Therefore, I want to start my acknowledgements by
thanking CutestSquirrel and CandyBar, who are both amazing entertainers and gamers.
Next, I want to thank Dmitri Williams for sharing all his nerd wisdom with me, whether
it be regarding games, research, or career advice. Furthermore, I want to express my gratitude for
Peggy McLaughlin’s expert advice on writing and publishing as well as supporting the initial
project that would later turn into this dissertation. I am also incredibly thankful for having had
the opportunity to get Dennis Wixon on board of my qualifying exams committee and that he
stuck around for my dissertation committee. His theoretical and practical advice were invaluable.
I’d also like to express my thanks to my family, mainly to my mother Swanni, who
vicariously suffered through all the highs and lows of dissertation research and writing. I’m very
thankful for having had the support of many friends and fellow doctoral students during this
daunting endeavor.
III
Table of Contents
Chapter One: Introduction and Research Interest ................................................................... 1
What is Twitch and how does it work? ....................................................................................... 2
Why Study Twitch and Gaming? ................................................................................................ 4
Research on Twitch ..................................................................................................................... 6
The Role of Women in Gaming .................................................................................................. 8
Main Questions and Hypotheses ............................................................................................... 10
Research Design and Methodology .......................................................................................... 16
Experimental design. ............................................................................................................. 16
Pretesting stimulus material................................................................................................... 18
Survey instruments. ............................................................................................................... 19
Behavioral measures. ............................................................................................................. 20
Sample and recruitment of participants. ................................................................................ 21
Organization of Chapters .......................................................................................................... 21
Limitations and Further Considerations .................................................................................... 22
Theoretical and Practical Implications ...................................................................................... 24
Definition of Terms ................................................................................................................... 25
Summary of Chapter One .......................................................................................................... 26
Chapter Two: Object of Study and Theoretical Framework ................................................. 28
Twitch – Channel Structure, Subscription Model, Profiles and Chat ....................................... 28
IV
Channel structure. .................................................................................................................. 28
Subscription model. ............................................................................................................... 29
Chat. ....................................................................................................................................... 30
Twitch channel layout. .......................................................................................................... 31
Celebrating new followers, subscribers, and donors via alerts.............................................. 33
Twitch as a Window into Gaming Culture................................................................................ 34
The Role of Women in Gaming Culture ................................................................................... 35
Rejection of the ‘gamer identity’. .......................................................................................... 36
Sexism in online games. ........................................................................................................ 37
Sexualized video game characters. ........................................................................................ 39
Lack of representation or misrepresentation of women in esports and gaming. ................... 40
The role of women on Twitch. .............................................................................................. 41
Why Do People Watch Twitch? ................................................................................................ 44
Consequences of Sexual Objectification and Common Gender Stereotypes in Gaming .......... 46
Reinforcing Stereotypes and Cultivating Sexist Opinions? ...................................................... 51
Additional Considerations and Theoretical Frames .................................................................. 55
The ‘Cool Girl Trap’ and the illusion of choice. ................................................................... 56
Summary of Chapter Two ......................................................................................................... 59
Chapter Three: Methodology – Observations and Experimental Design ............................. 60
Selecting Channels for the Experimental Manipulation ........................................................... 61
V
Stream size. ............................................................................................................................ 62
Corporate stream versus private stream. ................................................................................ 63
Game choices. ........................................................................................................................ 63
Size and angle of the camera image. ..................................................................................... 64
Streamers’ appearance. .......................................................................................................... 64
Channel design. ..................................................................................................................... 66
Chat interaction, subscriber and donor rewards. ................................................................... 67
Different Types of Channels and the Motivations to Watch Them .......................................... 68
Method ...................................................................................................................................... 69
Participants. ........................................................................................................................... 69
Sampling procedures. ............................................................................................................ 71
Sample size, power, and precision......................................................................................... 73
Research design. .................................................................................................................... 73
Measures and covariates. ....................................................................................................... 77
Experimental conditions. ....................................................................................................... 80
Control. .................................................................................................................................. 89
Summary of Chapter Three ....................................................................................................... 90
Chapter Four: Overview of Results .......................................................................................... 91
Descriptive Statistics Across Experimental Groups.................................................................. 91
Perception of Twitch Streamers ................................................................................................ 94
VI
Effects from Viewing and Objectifying Female Streamers .................................................... 103
Effects of viewing objectified portrayals on success criteria and sexist attitudes towards
streamers .............................................................................................................................. 111
Motivations to Watch Different Types of Twitch Channels ................................................... 112
General findings on motivations.......................................................................................... 118
Summary of Chapter Four ....................................................................................................... 118
Chapter Five: Discussion, Limitations and Directions for Future Research ...................... 120
Discussion and Interpretation of Results ................................................................................. 120
General differences for female and male streamers. ........................................................... 120
The impact of objectification on perceptions of different female streamers ....................... 121
The impact of objectification on positive female role models. ........................................... 125
The impact of watching female streamers on beliefs about female players. ....................... 127
Categorizing different twitch channels and why viewers might watch them. ..................... 131
Limitations .............................................................................................................................. 134
Directions for Future Research ............................................................................................... 138
Conclusions ............................................................................................................................. 140
References .................................................................................................................................. 147
VII
List of Tables
Table 1. Bivariate Correlations Between General Success Criteria for Male Streamers .............. 95
Table 2. Bivariate Correlations Between General Success Criteria for Female Streamers .......... 96
Table 3. Paired Samples T-Test Comparing Success Criteria for Male and Female Streamers ... 98
Table 4. Mean Comparisons for Skill, Success Criteria and Adjectives Between the Objectified
and Personalized Condition ........................................................................................................ 102
Table 5. Linear Regression Model Predicting on the Percentage of Overall PC Players
Participants Estimated to Be Female .......................................................................................... 106
Table 6. Linear Regression Model Predicting on the Percentage of Esports Competitors
Participants Estimated to Be Female .......................................................................................... 107
Table 7. Independent T-Tests for Participants’ Estimates of Which Percentage of League of
Legends Players is Female, Split by Gender .............................................................................. 109
Table 8. Paired Samples T-Tests Comparing Participants’ Ratings of Success Criteria for Female
Streamers Before and After the Viewing Phase ......................................................................... 112
Table 9. Overview of Differences Between Assumed Motivations for Participants Across
Channels ...................................................................................................................................... 114
VIII
List of Figures
Figure 1. Static panels on a professional male League of Legends player’s Twitch profile. ....... 32
Figure 2. Static panels and channel feed of the most successful female variety streamer’s Twitch
profile. ........................................................................................................................................... 33
Figure 3. Picture of the invented esports League of Legends player Al1ssa used in the
experiment..................................................................................................................................... 76
Figure 4. CandyBar’s channel profile ........................................................................................... 85
Figure 5. CutestSquirrel’s channel profile .................................................................................... 85
Figure 6. CutestSquirrel’s Twitch avatar (left) and CandyBar’s Twitch avatar (right) ................ 87
Figure 7. CandyBar while streaming ............................................................................................ 88
Figure 8. CutestSquirrel while streaming. .................................................................................... 89
Figure 9. Participants’ estimates of which percentage of League players is female depending on
whether they watched a female streamer or the control. ............................................................ 110
Chapter One: Introduction and Research Interest
A still continuously growing body of academic research, media reports, and reported
sales numbers has produced sufficient evidence for deeming video games (including consoles,
mobile games, and PC games) a mass phenomenon, deeply engrained in today’s popular culture.
Some online PC games (sometimes also referred to as networked games) not only have millions
of players but also developed an industry around competitive gaming. Esports events fill
stadiums and are even broadcasted on national cable TV. However, broadcasting major esports
events on national TV is a rather recent development and somewhat of a rare occurrence. These
are mainly reserved for events resembling the Super Bowl of esports. Smaller events, whether it
is the group phase of a smaller tournament or competitions in less popular games, are usually
live streamed via the several platforms dedicated to live streaming video games. The most
successful of these platforms is Twitch. On Twitch, not only big esports events are live
broadcasted. Most of the platform’s content is generated by users streaming their gameplay from
their homes. For many of these broadcasters, streaming is their main occupation. The more
successful broadcasters can make a good living off of playing video games for and with their
viewers. Through not only providing viewers with broadcasts of tournaments but also streams
that are entertaining or informative, Twitch has become an essential part of gaming culture. Each
channel, whether it is hosted privately or streams esports tournaments, has its own stream chat.
Viewers can interact with the streamer as well as other viewers. Therefore, Twitch can also be
seen as a place for community and communication. It is where gamers come together and watch
and comment on their favorite hobby, exchange ideas, laugh together, and try to improve their
gaming skills.
2
Despite its constant growth, gaming culture is not known for being very inclusive towards
minorities. It is often described as a hostile environment towards women (e.g. O’Leary, 2012)
and depicted to be not only sexist but also ageist and homophobe (Consalvo, 2012). Most
successful esports competitors as well as streamers are male. Female streamers are
underrepresented and often subjected to sexual harassment. Some female streamers, rather than
emphasizing the gameplay on their channel, have started to model their channels around their
physical appearance. Some female streamers have created a large following by being what the
community calls ‘boob streamers’. Male streamers complain about female streamers making
easy money by flaunting cleavage on stream while they have to work hard for their money.
Female streamers complain that they are not being taken seriously and sexually harassed because
‘boob streamers’ give them a bad reputation. This dissertation aims to apply cultivation theory,
mainly hailing from television studies, to investigate the effects of watching female streamers on
the perception of female gamers and women in general. It will argue that sexually objectifying
streamers might have an impact on how capable viewers perceive the streamers to be. An
experimental design manipulating the channel being watched regarding whether it features a
personalized or an objectified representation of a female streamer, is employed to study possible
effects of watching these streamers over the course of several weeks.
What is Twitch and how does it work?
Twitch started out as a small startup named JustinTV, which was then transformed into Twitch.tv
and acquired by Amazon in 2014 (Kim, 2014). While there are several less populated platforms
(in terms of content providers as well as viewers) specifically dedicated to streaming video
games, live streaming not only goes beyond these content restrictions but also conquered
mainstream social media and streaming services, such as Facebook and YouTube. Facebook only
3
recently implemented a beta version of a direct live stream from Blizzard Entertainment games
and YouTube extended their services from merely uploading videos to also live broadcasting
content through their platform.
But not all live streams of video games feature esports events. The majority of broadcasts
on Twitch and other streaming platforms, such as Hitbox.tv for example, are hosted by what will
be referred to as private streamers in this work. This refers to channels not hosted by esports
leagues and organizations, game developers or other non-private organizations. Every user
signing up for an account with Twitch is automatically assigned a channel. While not all users
are also content creators, many Twitch users are viewers and broadcasters, consumers and
producers at the same time. Each user’s channel consists of the gaming screen, which will
display the live stream when the user is online, a chat, a profile and a dynamic wall for
announcements. The most prominent and distinguishing factor of live streams as compared to
either TV or video on demand platforms, such as YouTube or Vimeo, is that it allows for
immediate feedback and social interaction. Therefore, each channel on live streaming platforms
not only serves as a live stream but also provides the structure for community and live interaction
between broadcaster and viewers as well as among viewers.
Twitch’s business model for streaming is rather simple: every user can stream on the
platform for free and watch for free. However, once channels reach a certain number of
concurrent viewers and accumulate followers, the platform offers partnership contracts to the
hosts of these channels. The streamer will receive a portion of the advertising revenue generated
through streaming ads on their channel as well as a cut from subscription fees. Viewers can
subscribe to partnered streamers for five dollars per month. In addition, many streamers set up
accounts to receive donations that work somewhat similar to tips. Subscribers usually receive
4
perks, such as emoticons or not having to watch commercials. While a portion (probably around
half) of the monthly subscription fee does not seem to be a lot of money, major Twitch streamers
can easily make a living by playing video games on live stream. In many ways, this appears to be
everyone’s dream job: being paid to play video games and accumulate fans from around the
world. Especially channels that have been successful for a few years now, often created their
own community. In a talk Hafu, a successful Hearthstone player and Twitch broadcaster, gave at
USC Annenberg earlier this year, she mentioned that she knows of at least three couples who
have met on her stream and who became long-term partners.
In 2014, Twitch crossed the line of 100 million unique monthly users (Twitch, 2015a). In
August 2015, two major esports events were held and streamed at the same time and the platform
peaked at 2,098,529 concurrent users (Twitch, 2016). The platform is continuously growing in
terms of viewership and the number of broadcasters. Esports had comparably impressive growth
rates. However, Twitch and the popularity of esports events are just a logical consequence of
games, in particular online games, have become a mass phenomenon. Ever since the success of
World of Warcraft and more recently League of Legends, two multiplayer online games that
have millions of active players (ESA, 2015), hardly anyone is still unaware of the immense
popularity of video games.
Why Study Twitch and Gaming?
Hamilton et al. (2014) present a first analysis of Twitch streams and provide a claim for
viewing each channel and the platform as a whole as a ‘third place’. The notion of the third
place, a place that has one major purpose: conversation was first introduced by Oldenburg (1989)
and then picked up by Putnam (2000). Both note the importance of such places for civic
engagement and democracy. Assuming that Twitch channels can be viewed as third places in
5
which members of the gaming and esports community come together and converse, an inclusion
and representation of all members appears to be crucial for them playing a role within the
community. The vast majority of users and streamers on Twitch are male. This reflects the
gender divide in the gaming and esports community as a whole. Although numbers released by
the Entertainment Software Association (ESA, 2015) show that women now play more than
men, these numbers mainly stem from mobile and social media games. It is very likely that these
numbers look extremely different for console and PC games and are more congruent with the
gender divide observed on Twitch (e.g. Yee, 2017; OPGroup.tv, 2015). Not even one of the ten
channels with the most followers, viewers, and subscribers for 2015 is hosted by a female
streamer (Socialblade, 2016). A similar situation can be observed in esports. Currently, the
highest earning female identifying esports athlete ranks at 287 in a ranking of total earnings from
esports tournaments (E-sports Earnings, 2017).
However, it is not only the discrepancy between female and male streamers’ and
competitors’ income that gives reason for concern. From traditional media (TV, magazines) we
know that representation matters. Seeing someone like you on TV is equivalent to feeling part of
society. The lack of representation of female gamers on Twitch is worrisome in regard to women
feeling part of the gaming community. While it might seem like that is not necessarily an issue
worth discussing, gaming and esports have been connected to the mastery of technology and the
accompanying empowerment. Several researchers have investigated connections between
playing video games and inclusion into the gaming community and children’s inclination to
pursue studies in STEM disciplines. Questions of empowerment, inclusion and representation are
important to researchers and consequently probably to policy makers. However, game
developers and distributers should be mindful of missing out on potential revenue because
6
gaming remains to be a male dominated activity and women often don’t identify as gamers
(PEW Research Center, 2015). If Twitch can fulfill a similar role for the gaming and esports
community that TV fulfills for society in general, a lack of representation of female gamers
might very well be connected with women feeling excluded and not welcome in the gaming
community.
For communication and media scholars, live streaming with its immediate feedback and
the community interactions in combination with a mass-media-like broadcast opens up a whole
array of questions. Countless studies investigate the connection between violent or sexually
explicit video games and aggressive behaviors or sexist attitudes (e.g. Ferguson, 2015, 2007; Fox
& Tang, 2014; Breuer et al., 2014; Dill & Thill, 2007). And even though the results of these
studies tend to be mixed, there is sufficient evidence for the presence of (limited) cultivation
effects from playing video games (Williams, 2006; Fox & Potocki, 2015). These studies focus on
whether cultivation effects exist for players of the game. But what if people only watch or even
watch and play these games? And on top of seeing the game, they also watch a streamer playing
this game and commenting on it. Can we observe similar effects? Or could there be social
learning effects (Bandura, 2001)? These are only some of the questions that might be of interest
to communication and media scholars in regard to video game live streaming.
Research on Twitch
Not many published works investigate live streaming in general and video game live streaming
and Twitch in particular. However, Twitch will merely represent the object of study for the
proposed dissertation. The analyses will be guided by theories and concepts employed to study
other forms of media and the relationship between media consumption and receiver attitudes and
perceptions. In 2012, Kaytoue et al. presented a first analysis of large scale Twitch data and
7
found that channel growth and popularity of streamers evolve in a predictable way. Hamilton,
Garretson and Kerne (2014) explored Twitch streams and communities and make a claim for
why they can be considered the kind of third place Oldenburg (1989) described. They took a
qualitative approach to an in-depth analysis of several Twitch streams and the communities they
fostered. Nascimento et al. (2015) studied a large sample of chat data from Twitch channel chats
to model chat activity, stream hopping, and churn. Pires and Simon (2015) presented analyses of
a large Twitch dataset including broadcasters and viewers as well as what games were played.
However, their analyses remain very much on the technical and descriptive side. The majority of
the few papers on Twitch are published in the proceedings of network and multimedia systems
based conferences, mainly focusing on the technological aspects of video game live streaming
platforms rather than the social scientific perspective. It seems like there has been a fascination
with the opportunities the behavioral data collected through Twitch might offer but less of an
interest into the quality of social interaction.
In a recent study on comments by Twitch users in channel chats, researchers found that
comments on female identifying streamers’ channels often included words unrelated to gaming
such as ‘boobs’, ‘babe’, ‘smile’, or ‘omg’. Viewers commenting in channels hosted by male
identifying streamers, however, often used words such as ‘melee’, ‘glitch’, ‘shields’, or ‘reset’
(Nakandala et al., 2016). These words refer to the gaming content the streamer engages with.
The researchers’ sample is rather dated and lacks control over alternative explanations for why
the investigated streams might encourage or punish certain words. Nevertheless, the
overrepresented words on female identifying channels are clearly pointing towards an
objectifying image of female streamers and demonstrate a lack of interest for their gameplay.
These results resonate with a preliminary study conducted by the author in 2014 which surveyed
8
Twitch viewers. The study found that the assumed criteria for success heavily differ for male and
female streamers. Viewers believe that skill at the games played as well as an interesting
personality are important factors for male streamers’ success while physical attractiveness is the
most important criterion for a female streamer’s success (Uszkoreit, 2015).
Another recent study investigates the motivations behind watching Twitch from a uses
and gratifications perspective (Sjöblom & Hamari, 2016). In a survey of n=1097 Twitch viewers,
the authors find a positive correlation between tension release as well as social integrative and
affective motivations and the number of hours participants watch Twitch streams. Social
integrative motivations, on the other hand, best explain subscription behavior. Despite the
authors’ efforts to explain motivations for watching Twitch, the lack of distinction between
different channels and respective motivations to watch them, appears to be diminishing the
explanatory power of this study. Given the wide range of content provided by different Twitch
streamers and what aspects of entertainment they focus on, it seems unlikely that motivations can
be explained without specifying which channels viewers choose to watch, follow, and subscribe
to.
The Role of Women in Gaming
“I would be amiss if I did not mention the bane of the Twitch community, the ‘gamer
girl’. Now I don’t mean girls who play the game normally, like the Hearthstone streamers
Hafu and Eloise. I mean girl gamers who flaunt massive cleavage and play League of
Legends poorly to anger the people watching. My advice is to avoid any streamer whose camera
on screen is bigger than the actual game (though you’ll check them out anyway.)” (Asarch,
2016).
9
Even though the numbers of women playing (and admit to playing) video games seem to
constantly be rising, they are still in the minority in the gaming community, especially networked
PC games and esports (e.g. Hartmann & Klimmt, 2006; Taylor, 2012; Shaw, 2014). Recently
published numbers reveal that while the gender split across all game genres might be almost
even, in most of the played networked multiplayer games, such as League of Legends or Dota2,
women only account for 10% of players. 7.2% of first-person shooter players are women and
23% of all World of Warcraft players are female (Yee, 2017). Gaming and esports – and now
Twitch, as a platform for these communities to come together and converse – are heavily male-
dominated spaces which are often considered hostile towards women and other minorities
including LGBTQ and people of color (O’Leary, 2012; Taylor, 2006; Yee, 2006; Consalvo,
2012). Studies find that not only are women less likely to identify as gamers compared to men
(PEW, 2015), they also consistently underreport their gameplay (Williams et al., 2009).
Identifying as a gamer and therefore as part of the community is not desirable for women. In
part, this might very well be due to social desirable opinions about gender roles (Taylor, 2012).
However, it is likely that being the minority in this male dominated community and often
exposed to stereotypical perceptions of women and sexual harassment does not make women
more inclined to identify with this community (e.g. Gray, 2012). Several studies investigate the
effects of gender on several aspects of social interaction within online games. Gray (2012)
presents an analysis of an online gaming community in which women, because they are
perceived to be in the vocal minority, are constant targets of sexual harassment. Ivory et al.
(2014) find that as soon as women reveal their sex, their legitimacy, competences and status are
questioned. Employing expectation states theory (Berger, Fisek, Norman, & Zelditch, 1977) they
report that because women are expected to remain quiet and friendly, friend requests are more
10
likely to be accepted when female players adhere to these expectations. Engaging in arguments
and speaking up correlates with denied friend requests (Ivory et al., 2014). The reluctance of
many women to identify as gamers although they play games is not the only issue noteworthy
here. Fox and Tang (2016) present findings that give reason to believe that the last resort many
women see for coping with sexual harassment in games, is quitting. While many will engage in
other coping behaviors first, consistently experiencing sexual harassment while playing can
cause women to quit online games. Fox and Tang (2016) emphasize the distinction between
general and sexual harassment in their study. One of the misperceptions of many males in the
gaming community seems to be that women are not able to deal with so called “trash talk”, a
form of general harassment. However, the authors find that general insults do not bother most of
the analyzed cases. It is not that women cannot handle the rough tone within this boys’ club of
gaming.
While not every gamer is automatically an esports fan, there is quite a lot of overlap
between the two communities. To become a professional gamer, you first need to become
exceptional at playing games which normally requires practice and other good gamers to play
with and against. Taylor (2012) notes that because it is so difficult for women to identify with the
community and find time, equipment, and players to practice with, there are significantly less
professional female gamers and they are significantly less successful. The discrepancy in income
mentioned earlier supports this point.
Main Questions and Hypotheses
There is little published research on Twitch, especially with a social scientific focus. Therefore,
one of the first questions this dissertation will address is how to describe the platform’s multitude
of channels and streamers in a way that allows for other future projects to build on. A set of
11
criteria for categorizing Twitch channels will be presented. It is likely that different channels
attract viewers for different reason. Therefore, different channels are assumed to cater to
different demands.
Research question RQ1: Which codes and cues might be helpful in identifying how
streamers and their channels can be categorized? Could different motivations to view them be
related to these criteria?
Several dimensions for this characterization appear to be rather obvious, such as whether a
stream is hosted by a private streamer or whether it is hosted by a corporation or an esports
organization. Other dimensions address the content: variety stream versus focus on a specific
game, or whether the private streamer is a ‘casual’ or a professional gamer. The size of the
concurrent viewership as well as interaction with viewers and form of community could also be
sensible dimensions to categorize streams. Answering this rather broad research question will
provide a framework for conducting further analyses but will neither be broken down into
hypotheses nor guide specific empirical methods. The implications for future research
investigating motivations for watching Twitch will be discussed.
Heflick and Goldenberg (2009) describe a connection between objectifying women and
perceptions of their competence, morality, and warmth. Objectifying women can lead to a
decreased perception of their competence. Judged by the way some female Twitch streamers
choose to present themselves and by the way viewers tend to comment on their appearance, it
would be sensible to assume that many viewers objectify female streamers.
Research question RQ2: Are viewers sexually objectifying certain female identifying
streamers?
12
The third research question addressed the relationship between viewing streams and
perceptions about the population of (online) gamers in general. Research on cultivation effects
from watching television or playing games give reason to believe that live streaming might
cultivate opinions about gamers and esports athletes. According to Gerbner and Gross (1986),
first-order cultivation effects refer to adopting certain (mostly numerical) facts while second-
order effects are changes in more general assumptions, opinions and attitudes. Watching female
streamers on Twitch could impact viewers’ perceptions about female gamers and esports
athletes. Female online gamers are often confronted with a multitude of stereotypes. These
include the notions that women are generally worse at gaming than men, choose to receive a lot
of help from male players, usually game with a romantic partner or tend to cause trouble in
groups or teams (Fox & Tang, 2014). According to findings on the effects of sexual
objectification of women, watching female streamers and sexually objectifying them could lead
to perceiving them as less competent. This corresponds to the most pervasive stereotype about
women generally being bad at gaming. Cultivation theory would suggest that heavy users, who
are confronted with reoccurring patterns, cultivate certain beliefs in regard to who plays online
games and opinions about female gamers.
Research question RQ3: Is watching certain female streamers related to perceptions about
female gamers and esports athletes in general?
The presented research questions RQ2 and RQ3 were further refined into
operationalizable hypotheses and tested on data collected through an online experiment.
Participants were instructed to watch an assigned Twitch channel over the course of several
weeks. Three survey waves collected data on their demographics, viewing behaviors, belief and
attitudes.
13
RQ1 addresses the types of channels that exist on the platform and what categories of
channels could cater to the intended experimental manipulation investigating the other research
questions. It will not be operationalized into a hypothesis but rather guide the generation of
criteria for categorizing Twitch channels. Based on these criteria, channels that reflect the
desired experimental manipulation were selected and pretested. Based on previous examinations
of related research questions on Twitch by the author, possible indicators for identifying a
streamer that is more likely to be sexually objectified by their viewers are the way the streamer
set up their camera (size of the image relative to gaming content, angle) as well as the rewards
for followers and subscribers and the degree of chat interaction (Uszkoreit, 2015). Heflick and
Goldenberg (2009) found that focusing on the aspect of ‘appearance’ rather than ‘person’ alone
had measurable effects on how the objectified version of a celebrity was perceived by study
participants. An obvious focus on appearance through camera image size, provided links to
social media, such as Instagram, could therefore be interpreted as cues for a streamer that is more
likely to be objectified by their viewers as compared to others. Gervais et al. (2012) used images
of chest and waist as ‘sexual body parts’ for their experiments investigating how women are
perceived as objects and why. They argue that these body parts are used by receivers to discern
biological sex and even though they are not primary sexual organs, the researchers found that
isolated images of women’s chests and waists were not associated with the whole body. In other
words, these body parts were sexually objectified (Gervais, 2012). These results give reason to
believe that despite the absence of nudity on Twitch, camera angles allowing for a view of the
chest and revealing clothing can be perceived as a focus on sexual body parts. Such a setup could
therefore be more likely to prompt sexual objectification. Based on these results and previous
observations of a variety of channels on the platform, the experimental manipulation will employ
14
streamers of varying degrees of likelihood to be objectified. A pilot-test for the stimulus material
and a manipulation-check for the experimental conditions will be presented in the methodology
section. Detailed thoughts in response to RQ1 will be discussed as part of the methodology
chapter three. Research question RQ2 will be hypothesized as follows:
H1a: Appearance will be ranked to be more important than personality or gaming skills
for female streamers’ success on Twitch.
H1b: Gaming skills and personality will be ranked to be more important than appearance
for male streamers’ success on Twitch.Items measuring these variables will be included in the
survey waves accompanying the online experiment.
The research presented by Heflick and Goldenberg (2009) on objectifying female personalities
on TV suggests that an objectified perception of female streamers would result in viewers
perceiving them as less capable, warm, and moral.
H2a: Viewers will rate an objectifying representation of a female streamer as less skillful
at the game(s) she is playing on stream compared to ratings of a personalized portrayal.
H2b: Viewers will rate the personality and gaming skills of an objectifying representation
of a female streamer as less important for success compared to ratings of a personalized
portrayal.
H2c: Viewers will rate an objectifying representation of a female streamer as less warm,
moral, and competent compared to ratings of a personalized portrayal.
15
Schooler’s (2015) experiment on sexually objectifying media representations implies that
sexual objectification may also have an impact on how viewers perceive strong and competent
(positive role model) females presented alongside sexually objectifying representations.
H3a: Viewers presented with a sexually objectifying image of a female streamer
alongside an image of a female esports competitor will rate the esports athlete as less likely to
succeed.
H3b: Viewers presented with a sexually objectifying image of a female streamer
alongside an image of a female esports competitor will rate the esports athlete as less competent,
warm, and moral.
These effects translate quite well into some of the stereotypes about female gamers – female
gamers are less capable, they are focused on seeking men’s attention and items that could help
them advance in the game. They happily use male players to help them acquire more items or
other achievements in the game (Fox & Tang, 2014). It would therefore be reasonable to assume
that repeated exposure to objectified female streamers could lead to viewers having more
negative perceptions of female gamers and possibly esports athletes. However, cultivation theory
would suggest that before changes in attitude take place, i.e. implicit and explicit sexist attitudes,
viewers’ beliefs about population estimates could be impacted. Therefore, RQ3 lead to
formulating the following hypotheses:
H4a: Regular viewers of female streamers will estimate the percentage of female gamers
to be higher.
H4b: Regular viewers of female streamers will estimate the percentage of female esports
competitors to be higher.
16
H4c: Regular viewers of female League of Legend streamers will estimate the percentage
of female League of Legends players to be higher.
In addition to investigating possible effects on the population of gamers, two hypotheses
predicting increased negative attitudes towards female gamers and women in general resulting
from watching and objectifying female streamers were tested.
H5a: Viewers who regularly watch sexually objectifying portrayals of female streamers
will have more explicit sexist attitudes towards female gamers than viewers of personalized
portrayals.
H5bc: Viewers who regularly watch sexually objectifying portrayals of female streamers
will have more explicit sexist attitudes towards women in general than viewers of personalized
portrayals.
Research Design and Methodology
The research design consisted of an online experiment and three surveys, an initial survey at the
beginning, one right after the experimental (i.e. the viewing phase) concluded and one follow-up
survey a few weeks later. The experimental manipulation was pre-tested to generate appropriate
stimulus material. Another manipulation check was part of the the second survey.
Experimental design. The manipulation of the independent variable for the experiment
was achieved through instructing participants to watch a specific streamer for three to five hours
per week over the course of four weeks. Participants were randomly assigned to one of three
conditions. Condition one (or objectified condition) was instructed to watch a female streamer
that was previously determined as more likely to be sexually objectified.. Participants in this
condition were also instructed to focus on her appearance rather than her gameplay or her
17
personality. Heflick and Goldenberg (2009) used these instructions to successfully create an
objectified and a personalized condition in their experiment without even presenting different
stimulus materials. Participants assigned to condition two (or personalized condition) were
instructed to focus on the streamer’s gameplay and her personality. An exploratory study by the
author found that physical appearance was negatively correlated to personality and skill at
playing the game when asking people about the importance of criteria for success (Uszkoreit,
2015). Participants in this condition were assigned to watch a streamer who was identified to be
more ‘personalizable’, i.e. who focuses her stream more on the gameplay and her personality
than wearing low cut shirts and talking about sexual body parts. The third condition served as a
control group. To ensure that all three conditions spend the same amount of time watching
someone play the same game and respond to the same survey questions, a third group was
assigned to view a tournament stream. All three channels mainly stream the same game, League
of Legends. A MOBA developed by Riot Games. The control condition was assumed to present
viewers with exciting and informative gameplay but not with a private streamer. Both streamers
in the treatment conditions play at a comparable skill level. The game provides a ranking system
which makes it rather easy to determine whether players are similar in skill level. In addition,
other criteria, such as followership and viewer numbers were kept relatively constant across the
two experimental conditions. These steps ensured a reasonable amount of control over
confounding variables. The effectiveness of the selected streamers in regard to whether they
actually can be perceived as sexually objectified or personalized representations was pretested in
a small pilot study. During the main phase of the experiment, participants were assigned to watch
their stream for three to five hours weekly. To obtain better control over the experiment, the chat
was logged during the time of the experiment.Since it was assumed that not all participants will
18
actually watch the hours instructed, the experiment suggested rather long hours to ensure
sufficient exposure to the stimulus material. Randomly checking viewer lists of the assigned
channels helped inform the researcher about whether participants were following the instructions
or not. Since cultivation or social learning effects are usually expected to require a certain
amount of exposure, participants were instructed to watch for three to five hours over the course
of four weeks. Obviously, watching for longer and for more hours was expected to produce
larger effects. However, streams change and evolve. Streamers might go on vacations or start
playing a different game. These factors could have impacted the experimental manipulation.
Therefore, viewers were not assigned to watch for longer than a month. Since previous
studiesdetected effects of objectification after a single exposure, watching a streamer for about
three hours each week over the course of four weeks was expected to provide sufficient
exposure.
Pretesting stimulus material. Prior to the experiment, a set of stimulus materials was
presented to a smaller sample of Twitch viewers. Pre-testing the material ensured that the
channels participants will watch in the different experimental conditions were perceived as
intended. Among the variables altered across selected streamers were the size and angle of the
camera image, the type of clothing, the overlay streamers use as well as information they
displayed on their profile. Obviously, manipulating these variables cannot take place in an
optimally controlled fashion since this is a field experiment and not a lab study.However, the
selected streams were chosen with based on a certain set of criteria for categorizing Twitch
channels. For each set of manipulated stimulus materials, respondents were asked to judge the
presented channels’ gaming skill, how important their hosts’ personality and their appearance are
for their success, and what motivation would entice them to watch their channel. In addition,
19
participants were asked to attribute certain adjectives to the presented streamers or images of
their channels. These adjectives were first used in a study by Fiske, Cuddy and Glick (2006) and
later on employed in another study by Schooler (2015) as a tool to detect and measure the degree
of sexual objectification. Five adjectives are referring to competence (competent, capable,
competitive, independent, and intelligent) and five to warmth (caring, compassionate, likeable,
warm, and welcoming).
Survey instruments. The experiment was accompanied by three survey waves assessing
demographic information, viewing and gaming behaviors as well as sexist attitudes (general and
in regard to gamers and esports athletes). The surveys also included items about viewing
motivations and objectification of female streamers. Even though a pretest-posttest design to
measure sexist attitudes would have been sensible to detect smaller differences, priming
participants with a whole scale to measure sexist attitudes before the experiment would have
likely results in several undesired consequences. These could have included reduced response
rates or participants purposefully responding in a more socially desirable manner.Randomly
assigning participants to treatment conditions and control condition was assumed to control for
possible pre-formed sexist beliefs. The initial survey also asked for demographic information and
viewing behaviors. Participants will were asked to provide their Twitch user name, how many
streamers they followed, which streamers they subscribed to, how long they usually watched
Twitch in general and what kind of games they played and watched. In the second survey,
administered directly after the conclusion of the four-week viewing phase, participants were then
asked what percentage of gamers and esports athletes they estimated to be female. These can be
assumed to be expected first-order effects resulting from possible cultivation. The second survey
also presented participants with an image of their streamer or a picture of professional players at
20
the tournament and an image of a female esports athlete along with a small neutral
announcement of her joining a competitive team. A set of items then prompted participants to
rate this esports athlete’s competence and warmth. Similar to the work presented by Schooler
(2015), this small part of the final survey was supposed to inform about the possible effects of
sexually objectifying media portrayals on supposedly strong, and capable female portrayals. Fox
and Tang’s (2014) sexism in video games scale was used to measure sexist attitudes towards
gamers. Several items will be slightly rephrased and repurposed for also assessing the sexist
attitudes towards female streamers. In addition, the ambivalent sexism inventory by Glick and
Fiske (1996) measured general sexist attitudes. Lastly, the second survey included a set of items
assessing whether the manipulation has been successful. These items will aim to identify whether
participants perceived the streamer as putting more emphasis on her appearance than the
gameplay. Participants will be asked what, in their opinion, makes their respective streamer
successful and why people would watch them. Since the effects of viewing a certain streamer
might wear off with time, another survey will followed up with participants an additional week
after the viewing phase concluded. The third survey was very similar to the second survey but
also included the set of items about general success criteria to create pretest-posttest
comparisons.
Behavioral measures. The stream chat of the assigned channels was monitored and
logged. Participants were asked to self-report on their viewing behaviors so that the statistical
analyses can control for the hours spent watching as well as possible other viewing activities. A
diary logging the times they tuned into their assigned stream was submitted as part of the second
survey.
21
Plans for data analyses. The differences between groups will be measured using analysis of
variance, t-tests and paired-samples t-tests. In addition, multiple regression models were
employed to further explore possible correlational relationships between variables. Comparing
groups on demographic variables as well as their viewing and gaming behaviors also ensured
that there were no significant differences between the groups that could have provided
alternative explanations for observed effects.
Sample and recruitment of participants. Participants for the experiment were recruited
via social media platforms (Twitter, Reddit, community websites for specific games etc.) and, of
course, Twitch. Participants had to be registered Twitch users, play online games, be 18 or older,
and Canadian or US citizens or permanent residents. A more detailed explanation of these
inclusion criteria will be provided in the methodology chapter. The study did not exclude women
from participating because previous research on the topic of objectification found that women
sexually objectify other women, too. However, analyses will be run separately to control for
possible gender differences.
Organization of Chapters
This introduction outlined the problem this study is going to address and how it will
proceed to do so. The second chapter will first delve into a detailed review of previous research
only briefly mentioned in the introduction. It aims to present and discuss studies investigating
gaming and cultivation effects as well as sexual objectification and its consequences. Obviously,
the sparse but growing body of research on live streaming and Twitch or other platforms will
contribute another subsection for this chapter. Lastly, critical discussions from a feminist
perspective on gaming spaces and the role of women in gaming and society will serve to provide
context and framing for the results and their implications. Each of these sections will conclude
22
with how they translate into variables and expected relationships in regard to the presented study.
This section will also include a closer look at the history of live streaming, the business model
Twitch offers, and the features of the platform, which will ensure providing the reader with
sufficient information about the object of study.
The third chapter will introduce the methodology used for this study. It will give a
detailed description of how the hypotheses are operationalized and the experimental
manipulation. This chapter will begin by presenting responses to the first part of research
question RQ1. Based on the presented literature and previous observations of the platform by the
author, what are factors and criteria for categorizing certain stream types and how can they be
described? Why might viewers decide to watch these channels? What are the motives? The
results from the pretest guiding the selection of specific streamers will be explained. Then,
chapter three will discuss the survey instruments used and describe inclusion criteria as well as
the recruitment process for participants in more detail.
Chapter four will elaborate on the findings and whether the presented hypotheses can be
confirmed or rejected. Finally, chapter five will 1) discuss these results, 2) relate them back to
the presented literature, 3) point out the contribution to the body of current research and d)
conclude in providing an outlook for future research opportunities.
Limitations and Further Considerations
Similar to most experiments, this study relies heavily on the success of the experimental
manipulation and control over possible confounding variables. Note that the study does not try to
control for differences in the stimulus material itself because it aims to create the most effective
sexual objectifying version of a female streamer possible. Hence it combines selecting streamers
who put more emphasis on their body (and sexual body parts) with the instruction to focus on
23
their physical appearance for the objectified condition and a more personalized version with the
instruction to focus on personality and gameplay for the personalized condition. Thorough pre-
testing of stimulus materials as well as a concluding manipulation-check are necessary to ensure
participants actually viewed their assigned streamer as intended. Possible issues impacting
validity could arise due to a lack of comparability of the control condition. Finding a ‘neutral’
stimulus in a natural setting is a daunting task. This also applied to the experimental
manipulation itself – by not employing a laboratory setting and streamers specifically designed
to alter relevant variables, the study aims to improve external validity but is obviously at risk of
losing some internal validity.
One of the major limitations of the proposed study is identifying which mechanism is responsible
for adopting certain opinions and attitudes. Control over social interactions between viewers and
other confounding variables will be difficult to achieve. Do participants develop implicit or even
explicit negative gender attitudes because they are confronted with a sexually objectifying media
portrayal or do they ‘learn’ from chat and interacting with others? It is likely that a combination
of several processes is taking place and it might not be possible to fully disentangle them.
Logging behavioral data (including stream chats and whether participants are actually actively
watching the stream) will help to achieve better control over such alternative explanations.
However, even with the chatlogs and the diaries provided by participants it might be difficult to
tell which viewers actively read the stream chat and actively watched and which ones zoned out
while ‘watching’.
Another major limitation for making cultivation claims will be the length of the experiment and
the fact that viewers will be instructed to only watch one channel. Cultivation effects arise from
being confronted with reoccurring patterns repeatedly. The limited time frame of an online
24
experiment might fail to yield effects strong enough to measure. However, at least first-order
cultivation effects can be expected to be found. For example, watching female streamers might
impact on estimates about the percentage of female gamers compared to the control condition.
Watching female streamers regularly can be expected to result in higher estimates of female
gamers in the population of online gamers.
Theoretical and Practical Implications
The main questions this dissertation plans to address are whether different types of
channels and streamers can be categorized based on a shared combination of characteristics and
whether watching and objectifying female streamers can have an influence on how viewers
perceive female gamers and women in general. The implications of these results are not only
relevant to live streaming in particular but also provide insights into the role of women in
gaming. It also applies existing theories formulated for other media to a new medium and tries to
draw conclusions about the relationship between consuming the medium and its effects on
perceptions of the (gaming) population. What television is in relation to the real world, live
streaming could, to some extent, be to gaming and esports.
One of the reasons for studying Twitch is to investigate whether communication and media
theories generated from studying more traditional media, mainly television, might also apply to
live streaming and where the commonalities and differences are. However, gaining a better
understanding of Twitch as an example for a live streaming platform and gender dynamics can
also provide important practical insights. At first sight, the practical application of research into
Twitch and gender might not be as obvious. Media matters. Representation matters. Media and
communication scholars do not doubt this anymore. The media consumed by gamers is Twitch
and streamers are, to some degree, representations of members of the gaming and esports
25
community. A lack of representation or misrepresentation of female gamers might have similar
effects on whether women feel accepted by the community and strive to be part of it as it has for
members of society. Why should anyone care about whether women want to be gamers or not?
Revenues from select major game franchises easily exceed Hollywood movie production
revenues. The gaming industry is constantly growing. Women not identifying as gamers is most
likely an indicator for them not seeing it as beneficial or socially desirable for them to be a
gamer. However, gamers buy games and not identifying as gamers and underreporting play
indicates that women do play but might play (and spend) more. Therefore, an inclusive and
welcoming community that embraces all genders, races, ages, and nationalities can be a key to
increase revenue. From a less corporate-oriented perspective, gaming and the mastery of
technology which include being familiar with PCs and using them in everyday life could also
lead to less inhibitions in regard to moving towards professions, such as computer science. Not
only surviving but thriving as a girl in a boys’ club can also empower young girls and women
and prepare them for careers in male-dominated fields.
Definition of Terms
The following terms will be used throughout this work and some will be explained in
more detail as part of the section on Twitch in chapter two.
Streamer or host. The streamer or the host of a personal Twitch channel is the person
mainly responsible for creating content on this particular channel. The term is not to be confused
with the hosts of an esports tournament.
Twitch-partner. Partners are not streamer’s significant others or gaming companions.
Twitch-partners are streamers partnered with the platform, i.e. who have entered a contract to
26
only stream on Twitch and in return are provided with the option to subscribe to their channel
and earn a percentage of the advertising revenue from their channels.
Stream chat. Stream chat is a chat placed to the right of the gaming screen which is
always available and accessible to signed-in users. It offers a variety of features for moderating,
a collection of channel specific and platform-wide emojis and the option to be made available to
subscribers of a certain channel only. It is the main place of conversation for Twitch channel
communities.
Overlay. The overlay are graphic elements displayed on the gaming screen but not part of
the game being broadcasted. Many streamers use it to announce follower or subscriber goals,
give shout-outs to recent subscribers or donators and to make their channel look more unique and
recognizable.
Summary of Chapter One
Video game live streaming has become an essential part of gaming culture. Gaming and
streaming have become increasingly professionalized. Still, gaming culture as well as
professional gaming and streaming remain heavily male-dominated. This does not only prevent
women from participating in gaming activities but also from gaining lucrative positions in
esports or the gaming industry. Watching and objectifying female Twitch streamers could lead to
more negative perceptions about them and female gamers and women in general. These effects
could also impact on how they treat women in online gaming spaces as well as on Twitch, which
would in turn lead to more limitations to female players’ participation in gaming culture. Given
that media representations matter and can influence how people perceive the real world around
them, achieving an adequate representation of women on Twitch and creating visibility for them
could have a variety of positive effects. These might even include future female gamers seeking
27
to professionalize their gaming or pursuing careers in the gaming industry. Another hypothesis
this study plans to investigate is whether watching female gamers makes viewers perceive
women as less of a minority in online games. Not having the notion of women being in the
minority would make them less of a target of harassment and other in- versus out-group
behaviors.
Chapter two will start by providing a more detailed introduction to Twitch and will then
lead into a review of the relevant literature. It will present studies and concepts by disciplines
ranging from social psychology, feminist studies, and game studies to communication studies.
This second chapter will equip the reader with the necessary framework to follow how the
research questions were posed and refined into operationalizable hypotheses and a sufficient
understanding of the object of study.
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Chapter Two: Object of Study and Theoretical Framework
This chapter will further elaborate on the theoretical framework leading to the research questions
and hypotheses guiding the analyses. It will start out by providing a closer look at the object of
research, Twitch. Several aspects of the platform and how Twitch’s business model works are
relevant and necessary for understanding the criteria used to achieve the experimental
manipulation. After describing the platform in more detail, the chapter will present relevant
theories and previous research conducted on the topic of gender in gaming, objectification
theory, cultivation theory, and (post)-feminist theory. The literature review will start out with a
broader scope, presenting an overview of the general issues women face in gaming and esports.
It will then utilize this understanding of the general role of women in gaming to connect these
observations to objectification and cultivation theory which will lead to the proposed hypotheses.
While the chapter will have a strong focus on academic research, it will also utilize journalistic
reports on female streamers and women in gaming.
Twitch – Channel Structure, Subscription Model, Profiles and Chat
The first chapter already pointed out a few numbers indicating just how popular Twitch has
become over the past six years. While the business model as well as a few salient terms have
been briefly mentioned and explained during the introduction, a more detailed look at the
platform will be necessary to explain certain decisions that led to the presented research project.
Following a more structural view of how code dictates behaviors (e.g. Lessig, 2006), this section
will use the detailed description of the structures to shed some light on the resulting behaviors
and rituals.
Channel structure. Each user registering an account with the platform automatically
creates their own channel. The platform makes no distinction between content consumers and
29
creators. All currently broadcasting channels are listed under a certain content category. In most
cases, the streamer will list their channel under the game currently played on the live stream.
However, moving away from solely featuring gaming-related content, Twitch recently added the
categories Creative, IRL, and Social Eating
1
. The default setting for sorting channels within each
content category is by current number of viewers. So, by default, Twitch employs a ‘the rich are
getting richer’ principle to presenting content. This entices streamers to create thumbnails of
their streams for the channel overview page that generate as much attention as possible. The
stream title is also often used as ‘clickbait’ to lure more viewers onto a channel. Depending on
how many streams with high viewer count are online, getting high current viewer numbers can
push a stream to the top of the page. From there on, it will receive even more clicks. Apart from
inviting stream titles and thumbnails, a host from a channel with a high current viewer count can
also catapult a channel to the top of the page. Networking with other, preferably more successful,
streamers is therefore an important criterion for success since a single host can not only add large
numbers of followers but also create high visibility instantly. Whenever a streamer is hosted by
another channel, they will try to receive as many follows from new viewers as possible. The
number of followers does not only influence concurrent viewer count but can also impact
whether a streamer will be accepted for partnership.
Subscription model. On the partnership application website, Twitch specifies that to
qualify as a Twitch partner you need to have an established audience that constantly grows, a
regular streaming schedule of at least three hours per week, and content that is sanctioned under
the code of conduct. These are the minimum criteria for applying for partnership. It is
1
Since this work seeks to identify possible effects on the perception of female online gamers and
streamers, these channels will not be described in more detail for now. However, in regard to research on female
streamers the non-gaming categories surely hold the potential for exciting and impactful projects.
30
noteworthy that Twitch does not publish a specific number of followers or concurrent viewers a
channel is required to have in order to qualify. Twitch partners have an additional function added
to their channel, the ‘subscribe button’. Twitch partners usually receive about 50% of the
monthly subscription fee (which is $5 in the United States). The percentage varies on an
individual basis. More successful streamers or streamers who are part of esports organizations
will usually receive a higher percentage than freshly partnered broadcasters. While streamers,
especially more popular female streamers, not solely rely on subscription fees for their main
income, money generated from subscribers is still the steadiest source of income for them. Some
months, streamers will receive high donations that probably outweigh the income from
subscription fees but as streamers point out, they cannot rely on donations
2
. Therefore, a full-
time streamer’s main goal is always growing their subscriber numbers and motivating
subscribers to renew their subscription month after month. Again, the rich are getting richer and
amassing subscribers and building a community around your channel will always lead to more
visibility due to high current viewer counts and resulting visibility. In addition, Twitch gives
partnered streamers the option to create subscriber emoticons specific to their channel.
Depending on how many monthly subscribers a streamer has, the number of available emoticon
slots increases from two to fifty. These emoticons, as will be described in more detail in the chat
subsection below, are often a major incentive to subscribe to a streamer.
Chat. The live chat function alongside each broadcast is one of the most defining
features of live streaming on Twitch. It provides the bases for community and a direct feedback
loop to interact with the broadcaster. Twitch provides a plethora of global emoticons which can
2
Successful Hearthstone streamer Itshafu and World of Warcraft streamer Naguura both commented on
this during broadcasts on their channels.
31
be used in every stream chat by every registered user. Users can also choose to add certain
badges to their name. Some of them are global (e.g. Amazon Prime users can choose to have a
little crown next to their name as an incentive for users to connect their Amazon to their Twitch
accounts), others channel specific. The channel specific badges are very important since they
represent another major incentive for subscribing to a channel. Subscriber badges are displayed
next to a chatter’s name whenever they chat in the respective channel’s chat. Subscriber
emoticons provided by a certain channel can be used globally by subscribers. The subscriber
badge can therefore be described as a symbol signalizing to others that the user is a fully-fledged
member of the community. Rather recently, Twitch implemented loyalty badges to underline a
user’s status and their loyalty to the channel. These loyalty badges indicate that someone is a
regular in a stream community. Most streamers will also award long-term supporters with more
attention which can also serve as an incentive to subscribe. While subscriber badges are only
displayed if they user chats in the channel they are subscribed to, subscriber emoticons can be
used globally. Whenever subscribers use a streamer’s channel emoticons in other chats, it is
essentially free promotion for that channel. In addition, it can help members of the same
community to identify each other. Therefore, subscriber emoticons are very important tools for
promoting community but also promoting the channel globally. Twitch chat with its signifiers for
community membership is the main place for conversation and community building.
Twitch channel layout. Every channel consists of the stream window, a small channel
avatar, a profile banner, the stream chat and static panels with information about the streamer
(optional). As of late, Twitch is also working on developing a dynamic news feed which allows
streamers to post messages their followers can see on their start page. This indicates that Twitch
sees itself as a social media platform in addition to just being a live streaming platform. Most
32
streamers will display their schedule, information about their gaming gear, other social media
channels, and sponsors on their profile. Many streamers also choose to have a ‘frequently asked
questions’ section to anticipate the most common questions so that they do not need to respond
to them while broadcasting. Whenever a channel is live, the stream window will usually show
the gaming screen and, if the streamer chooses to use a camera, a webcam feed of the streamer
playing the game. Live streaming software allows the broadcasters to arrange whichever
windows they choose to stream and their camera feed as well as stream overlays with
information about donors, subscribers, or the game.
Figure 1. Static panels on a professional male League of Legends player’s Twitch profile.
33
Figure 2. Static panels and channel feed of the most successful female variety streamer’s Twitch
profile.
Celebrating new followers, subscribers, and donors via alerts. Per default, Twitch
sends an alert to a channel whenever someone follows or subscribes. In addition, several bots
and software solutions were developed to create customizable notifications for all kinds of events
during a live stream. Most common are subscriber and follower alerts that are often connected to
a sound, or a gif or video file, and will display the name of the user who just followed, donated
or subscribed. Twitch also allows users to include a brief message whenever they (re-)subscribe
to a channel. Oftentimes, this message will be displayed on the screen, read out loud by the
streamer or text to speech software, and Twitch chat also automatically pastes an alert into the
channel chat. All these features draw attention to the viewer who just supported the channel and
celebrate them as a member of the community. New or recurring subscribers are also often
greeted by the community with one or several of the custom-made subscriber emoticons. This
34
gives all members of the community the possibility to participate in honoring the supporter of the
stream. These rituals celebrating users are very common and are an essential element of channel
communities on Twitch. How streamers choose to present themselves on their stream, which
thumbnail and title they select, what elements they add to their channel profile, how they choose
to design their profile page and stream overlay, and which subscriber emoticons and alerts they
create all play a role in how a regular user of the platform appraises and categorizes the stream.
These elements will become relevant when research question R1 (which codes and cues might be
helpful in identifying how streamers and their channels can be categorized?) will be addressed in
the beginning of the third chapter. A clear understanding of how elements can help grow a
channel’s following is essential to understanding certain behaviors influenced by the structure of
the platform.
Twitch as a Window into Gaming Culture
Why would studying Twitch be relevant for advancing communication and media
research? Similar to television, Twitch can be understood as a window into gaming culture. It is
a place where gamers come together to learn, converse, and cheer for their favorite team.
Through understanding its codes and rituals as well as its effects on streamers and viewers alike,
scholars can learn about live streaming, esports, and gaming culture. The structural changes
Twitch has been introducing to their platform lately indicate that they want to emphasize the
social aspect of Twitch. This resonates with an early exploration into Twitch channels by
Hamilton et al. (2014) who found reasonable arguments for classifying them as third places.
Third places, as defined by Oldenburg (1997) are places that are neither home (first place) nor
the workplace (second place) but rather public spaces with the main purpose of conversation.
The authors drew on qualitative interviews with streamers and viewers as well as observations
35
from a multitude of channels. They came to the conclusion, that each channel fosters a unique
community. Twitch channels, Hamilton et al. (2014) argue, can be seen as third places because
similar processes are at play. Each Twitch channel has regulars, members of the community that
have been around for a while and achieved a certain status. In addition, Twitch chat, due to its
anonymity and invisibility of actors features a leveling function. Members are not judged by
their status in the first or second place (i.e. judged by their age, experience, or job status) but
judged on their gaming skills or what they contribute to the community, depending on the values
of the specific community. Twitch channels can therefore be described as places where members
of the larger gaming community come together, form smaller sub-communities, and converse.
As mentioned briefly in the subsection about the object of study, the Twitch platform, the
developers try to enhance the affordances catering to demand for social interaction. In addition,
content creators are gamers. They create gaming-related content for other gamers to watch.
The Role of Women in Gaming Culture
A constantly growing body of literature is invested in observing and describing the
phenomenon that is the systematic exclusion of women and other minorities from gaming
culture. Consalvo (2012) described gaming culture as sexist, ageist, racist, and homophobe. The
first computers were built and programmed by (mostly white) men. The first video games were
programmed and played by men because only research institutions in male dominated fields even
had computers. Many computer scientists saw video games more as part of their research than
foreseeing them to be entertainment media for the mass market (Donovan, 2010). Mastering
technology is seen as a means to display masculinity (Dunbar-Hester, 2008) and therefore,
mastering games and excelling at esports can also be understood as a demonstration of one’s
masculinity (Taylor, 2012). Taylor (2012) notes that another dimension of displaying
36
masculinity in gaming and esports is the traditional notion of perceiving an athlete, a competitor,
as strong and dominant, as well as masculine. Historically, gaming culture, ruled by nerds, geeks
and more recently athletes, is seen as male dominated. Nevertheless, more recent statistics
released by the Entertainment Software Association (ESA, 2015) and the PEW Research
Institute consistently state numbers closer to an even gender split than the oftentimes perceived
female gaming minority (PEW, 2015).
Rejection of the ‘gamer identity’. According to a survey conducted by PEW Research
Center in 2015, 50% of adult men report playing video games and 48% of adult women.
However, only 10% of the portion of the population stating to play video games identify as
‘gamers’. About 15% of the 50% of men who play video games also identify with being a gamer.
However, only 6% of the 48% of women playing video games would call themselves gamers
(PEW, 2015). Several factors are likely to play a role in this rejection of the gamer identity by
almost all gaming women. ‘True gamers’, for one, are known to not accept games on social
media platforms or smart phones as legitimate games for gamers (Scharkow et al., 2015). The
term ‘gamer’ is also often reserved for people who see themselves as hardcore gamers. Players
who do not play frequently enough or do not play the accepted games are not perceived as ‘true
gamers’ (Shaw, 2012). Since many women play games on social media or mobile games, the
‘hardcore’ branch of the gaming community excludes them per se. Fox and Tang (2017) pointed
out that this exclusive gamer identity can be named as one of the predictors for sexual
harassment. Women not identifying as gamers and therefore automatically perceived as an
outgroup by the mainly male gaming community makes them a target for sexual harassment and
abuse.
37
Sexism in online games. Women are still perceived to be a minority by most male
gamers. For some popular online games, such as first-person shooter games (FPS) or multiplayer
online battle arenas (‘MOBAs’), this might very well be true (Yee, 2017)
3
. Studies found
differences by gender in the motivations (Yee, 2006; 2012) for playing online games and in the
allotted time for gaming (Williams et al., 2009). However, Williams et al. (2009) also found that
women consistently underreport the time they spend gaming. One explanation the authors
provide for the observed behavior is that gaming is still not a socially desirable activity for
women. Online, the group identity often becomes more salient than the individual identity due to
a lack of available social cues (Postmes, Spears & Lea, 1998). The social identity model of
deindividuation effects (SIDE) suggests that players will more often fall back onto their male
gamer group identity in the absence of individual cues. Therefore, any cues that point towards
the gender of a person become salient (voice, female names, avatars, presumably female use of
emoticons and other linguistic cues) and can be used to determine whether someone is a member
of the in-group (male) or the out-group (non-male, female, etc.). Being perceived as part of the
out-group can give way to a multitude of other behaviors and expectations about other people’s
behavior. Being seen as the minority is likely to exacerbate the possible negative consequences.
Holz Ivory et al. (2014) conducted a study on whether people react differently to female and
male players in an online game. They used recordings to achieve a high level of control and only
altered the voice recordings, not the playstyle. The authors found that in the absence of richer
social cues, such as visual cues, voice and therefore the sex this voice can be associated with,
becomes a salient cue for determining whether another player is placed in an in-group or an out-
group (Holz Ivory et al., 2014). Once the player’s voice is identified as female, the other players
3
The data published on Yee’s company blog present convincing numbers. However, they are to be taken
with a grain of salt since it is unclear how representative the collected data is.
38
have a set of expectations of how the female player should behave. If the supposedly female
player voiced criticism or commented negatively on other gamers’ playing, the other players
were less likely to confirm a friend request. Whenever the female player made affirming
statements and was amicable and friendly, other players were more likely to comply with friend
requests by the female player. Holz Ivory and her collagues (2014) did not find any significant
differences in the outcome variable, complying to friendship requests, for the male voice. The
authors therefore concluded that players have the expectation of female players to be friendly
and amicable whereas male players can be critical and still be perceived as worthy of being
added as a friend. In an online game, complying with a friend request can also be understood as
the intention to play with someone again so rejecting someone’s friend request means players do
not have an interest in gaming with them again. Studies like these demonstrate that whenever a
player is identified as female, whether this is through voice or just by associating them with
certain female sounding user names, other players expect them to behave differently.
Even if the notion of women being a minority in most games might be incorrect after all
(or at least exaggerated), they are still in the vocal minority (Gray, 2012) and expected to behave
more amicable and less critical or assertive (Holz Ivory et al., 2014). To fly under the radar of
potential sexual harassers online, many women gamers choose to use less communication and
often end up avoiding voice communication all together (Fox & Tang, 2016). Other coping
mechanisms for sexual harassment described by Fox & Tang (2016) are gender-masking by
choosing male or neutral sounding user names or, in extreme cases, withdrawal from playing.
While these are feasible techniques that can help avoid sexual harassment, these coping
strategies lead to women remaining in the shadows and therefore, keeping up the possibly
incorrect illusion of them being a small minority in most games.
39
Sexualized video game characters. Not only are the women playing online games often
the target of benevolent and hostile sexual harassment, they might also be thrown off by
sexualized video game characters featured in many games. A recent study concluded that main
characters (often the playable characters in games) are less often depicted in a sexualized or
sexually objectifying manner these days. However, the same study also found that secondary
characters still appear as mostly hypersexualized representations without agency (Lynch et al.,
2016). According to Dill and Thill (2007), the highly sexualized female representations in video
games (often opposed to depicting male characters as muscular, strong and powerful) can lead to
teenagers accepting these as stereotypical presentation of sex roles. Fox and Potocki (2015)
found that playing video games was connected to hostile sexism and higher levels of rape myth
acceptance. Since the gender representations described by Dill and Thill (2007) as hyperfeminine
and hypermasculine can be found in a large percentage of games or advertisements for games,
Fox and Potocki (2015) interpreted their results as possible cultivation effects. Other cultivation
effects will be described in more detail later in this chapter. Research on video game characters
and how gender is depicted in games is not directly related to the subject of live streaming and
gender and therefore the main questions this work plans on addressing. However, the way female
video game characters are usually designed and the amount of agency and power they are given
(or more often the lack thereof) is another puzzle piece of the bigger picture of the role women
play in gaming culture. Even though there is evidence for both, cultivation effects (e.g. Fox &
Potocki, 2015) and the absence of them (Breuer et al., 2015), the hyperfeminine depictions of
women in games speaks to what kind of games are in demand and who mainly produces these
games.
40
Lack of representation or misrepresentation of women in esports and gaming.
Similar to the lack of strong female role models as game characters up until recent years (Lynch
et al., 2016) female gamers and esports competitors are still lacking representation. Gaming
events, whether conventions and tournaments or live broadcasts of these events, often lack
female gamers or even moderators and interviewers. The North American League of Legends
Championships (‘LCS’ or ‘NALCS’), one of the most popular esports leagues with sponsors,
such as Coca-Cola or American Express, fails to even have one woman in their line-up of nine
casters, hosts and analysts
4
. The European LCS features one female host (out of a team of eight).
So far, only one female player has ever qualified for the North American LCS with her team
(LeJacq, 2015). However, Maria “Remilia” Creveling also decided to step down before the
season even started. In her own words, she did not want to be “put on some lgbt agenda”
(LeJacq, 2015). While the reasons for her stepping down from her position on the team do not
matter here, it still puts an emphasis on the obvious lack of female representation in esports.
According to a website collecting information about prize money esports competitors received,
the highest earning female competitor of all times is Sasha “Scarlett” Hostyn, a professional
Starcraft II player from Canada with a total of $174,537.83 in earnings (esports earnings, 2017).
While this makes her the most successful female player by a large margin (the second highest
earning player accumulated around half of that), overall, she only places 293. 292 male players
have earned more over the course of their gaming careers with a maximum of total earnings of
$2,727,796.47 by American Dota 2 player Saahil “UNiVeRsE” Arora. The American
Hearthstone and World of Warcraft player Rumay “Hafu” Wang, ranked 12th in highest earnings
by female players, spoke very openly about the kinds of sexism she faced during her gaming
4
Analog to traditional sports, esports features commentators that do live commentary while the games are
being played (casters) as well as a team of analysts discussing certain key moments and replays after each match.
41
career. In an interview for Fusion magazine, she described her feelings towards being the only
girl playing at any gaming tournament she ever attended and the kind of comments she received
from other players, ‘fans’, or viewers watching her stream (Roose, 2016). Not only did she admit
to not feeling part of the gaming community that has not been welcoming to her, she also
recounts events during her early career where an opposing team renamed their team to “Gonna
Rape Hafu at Regionals”. Some of her Twitch viewers donate money while she streams only to
insult her (Roose, 2016). One of the major issues for female esports competitors and streamers
Hafu mentions is that the gaming community in general, and the Twitch community in particular,
seem to have a clear picture of how women are expected to behave within these communities.
The role of women on Twitch. If only very few women successfully compete as players
or work as casters and analysts, what professional roles are left for women to excel in? Most
professional gaming women make a living off streaming on Twitch. Despite some rumors, there
are still less female Twitch streamers than male streamers. The platform does not publish data on
the gender split of their content creators. However, according to Twitch, their viewers are 75%
male and 73% of them are between the ages 18 and 49 (Twitch, 2015). A study conducted by
moderators of mid-sized female streamers in the name of the ‘Online Performers Group’, an
agency representing Twitch talent, looked at data from the top 2500 Twitch channels and an
additional random sample of channels with 15+ viewers (OpG, 2015). Based on their coding of
these data, approximately 20% of channels are hosted by a female streamer. Compared to the
viewer numbers released by Twitch, these numbers seem quite reasonable. The study also aimed
to reject several other ‘myths’, as the authors call it, about female streamers. These include that
a) women are taking over Twitch and b) they are becoming more successful than male streamers.
The study concluded that only 10% of the top 500 channels are hosted by female streamers and
42
that men stream five times as many hours as women. While female streamers get more page
views and followers than the average male streamer, they gain less concurrent viewers (OPG,
2015). This could in part be due to them streaming less hours in one session. Nevertheless, the
numbers vary so heavily (20 average concurrent viewers gained over the course of 60 days for
male streamers as compared to 2 average concurrent viewers gained for female streamers) that
streaming five times as many hours does not seem to be the only plausible explanation for the
observed differences in viewer growth. In other words, female streamers get more clicks on their
stream and users are more likely to follow them but they do not stick around to watch the stream
for an extended amount of time and probably will not return in the future. The study did not state
any statistics on differences in subscriber numbers but the results for concurrent viewer growth
imply that female streamers will also have fewer subscribers and therefore earn less money.
However, many Twitch viewers (as can be seen in posts on reddit or various other social media
and gaming media) seem to believe that women can make an easy living on Twitch by exploiting
the pre-dominantly male viewership of the platform. This assumption is likely based on as well
as constantly perpetuating a stereotype of a female Twitch streamer commonly known as ‘titty
streamer’ or ‘boob streamer’. The stereotype describes a female streamer who usually has a large
webcam feed, her camera angled to show her upper body rather than her face, wearing low cut
shirts and showing lots of skin. Often, these streamers are presumably bad gamers or not ‘real
gamers’ and only streaming for money and attention. The interesting part about so-called boob
streamers is that while they receive a lot negative feedback and commentary in chat as well as on
Reddit or Twitter from men and women alike, the most successful female streamers can probably
be categorized to at least partially align with this stereotype
5
. In the interview Hafu gave for
5
Examples for successful streamers known to show a lot of cleavage are Kaceytron, LegendaryLea, or
KittyPlaysGames.
43
Fusion, she mentioned that often Twitch users will expect women on Twitch to show skin, laugh
of sexist and objectifying commentary, and spend more time interacting with viewers than
playing game (Roose, 2016). Many other female streamers seem to share this opinion as can be
deducted from following conversation on Twitch or the r/Twitch subreddit. Here, a female
streamer posted the following:
“I was streaming, I had my regular 7ish viewers, someone new came in. Asked me to turn
on my camera so I did. Then they proceeded to "demand" that I show cleavage and that unless I
did I wasn't a female.
I didn't do it. They got pissed threw some words I'm not going to repeat and left.
Females who purposely aim their cameras down their shirts are ruining it for those of us
who don't. Most viewers expect that cleavage shot because of the fact that the "top" female
streamers all seem to do it.
They think it is the norm. I really wish twitch would crack down on these types that way
female streams can be seen as something more than a cleavage shot who has gaming content
somewhere.” (Calitika, 2015)
While this quote only provides anecdotal evidence, a recent study analyzing chat logs from
Twitch channels found that the more popular female streamers all receive a large amount of
sexist and objectifying comments (Nakandala et al., 2016). This resonates with the latter part of
the comment by Redditer Calitika: the community thinks female streamers are almost obligated
to show cleavage and demands this of female streamers. Sexist language is the norm (Nakandala
et al., 2016). Male viewers, female streamers, and male streamers all criticize and shame the
‘boob streamers’ for different reasons. Nevertheless, they all seem to agree that this stereotype
44
exists and has a negative impact on their success or viewing experience. The question that
remains here now is why are so many female streamers successful employing some of the
described strategies for gaining viewers if no one likes them? Sex apparently still sells.
Especially if more than 75% of your audience are men, most of them in their late teens and early
twenties (Twitch, 2014). Many female streamers, such as Hafu or Naguura, the currently most
successful female World of Warcraft streamer and one of the few female players in the top
guilds, do not fit the described stereotype of the ‘boob streamer’. It would be wrong to think of
Twitch as a platform only displaying scandalously clad dressed female streamers who are terrible
gamers. Nevertheless, all female streamers, no matter how successful their track record as
esports competitors or how popular and funny they are as entertainers, are often confronted with
prejudice partially arising from the omnipresent negative and sexist stereotypes about female
streamers. These resonate with common stereotypes about female gamers and are anything but
new to the gaming community.
Why Do People Watch Twitch?
Expanding on what Hamilton et al. (2014) concluded from their observations and interviews with
streamers and viewers alike, Twitch is as much a place for community as it is for watching other
people play. Sjöblom and Hamari (2016) conducted a survey employing a uses and gratifications
theory approach. As first introduced by Katz, Blumler, & Gurevitch (1974), uses and
gratifications theory suggests that audiences actively select which media content to consume
based on the expectation it will satisfy a certain need or demand. These needs can engulf a
multitude of motives, such as affective and cognitive demands, the need to relax and release
tension or simply to be entertained. Based on items measuring motivations to watch sports on
television and consume other media they derived a set of items to explore why people tune in to
45
watch others play video games. The distinct motivations they examined were cognitive,
affective, social integrative, personal integrative, and tension release. They found tension release,
social integrative and affective motivations to predict how many hours people watch streams.
They also found social integrative motives to be the best predictor for subscription behaviors
(Sjöblom & Hamari, 2016). In part, these findings resonate with what Hamilton and his
colleagues concluded after interviewing streamers of different degrees of popularity. The
community each Twitch stream fosters around its channel is usually very distinctive and heavily
influenced by the streamer and the regulars, the viewers and subscribers tuning in day after day.
However, Hamilton et al (2014) also found that there is a certain point in which a stream grows
beyond that point in which the regulars know each other and chat messages trickle in at a pace
that allows viewers to follow the chat easily. There are a multitude of factors that might
incentivize subscribing to a streamer and they could be relying on structural aspects, such as the
popularity of a stream or whether the focus of a stream is on one game the streamer is an expert
in or a variety of games. Sjöblom and Hamari (2016) fail to distinguish certain types of streams
and motivations to watch them but rather try to find a one-size-fits-all concept to determine why
people would watch Twitch in general. As implied by observations made by Hamilton et al.
(2014) or the author herself (2015), Twitch channels might be so versatile that different criteria
will entice people to watch them and subscribe to them. While the result that social interaction
might be the main driver to become a regular supporter of a stream is certainly reasonable, the
authors could only explain a very small portion of the variance in subscription behavior by this
motive (R2=0.037). It can be assumed that other factors play a role and that these factors might
be related to the type of channel. In response to research question RQ1, chapter three will
46
provide a more detailed analysis of possible criteria for categorizing channels. It is sensible to
assume that different stream categories might also attract viewers for different reason.
RQ1: Which codes and cues might be helpful in identifying how streamers and their
channels can be categorized? How do motivations to watch the different types of channels vary?
The second part of the question will be addressed as part of the survey while the first part will be
not be tested empirically and merely addressed as part of the presented criteria for categorization.
Consequences of Sexual Objectification and Common Gender Stereotypes in Gaming
Research question RQ2 inquired whether viewers sexually objectify certain female
streamers and why. Given the omnipresence of sexual objectification of (mostly) women in
today’s society (APA, 2007; Frederickson & Roberts, 1997) it is rather self-evident that many
viewers will sexually objectify a young woman playing video games on camera. Asking research
question RQ2 was nevertheless necessary to lay out the groundwork on which later analyses of
effects are based. Chapter three will begin by identifying certain criteria users might employ to
categorize channels. This first analysis based on observations framed by theories presented in
this chapter provided the ideas for which streamers might be more likely to be sexually
objectified. Sexual objectification is defined as reducing a person, in most cases a woman, to an
object by way of disconnecting her multidimensional personality from her sexual body parts
(Bartky, 1990; Frederickson & Roberts, 1997). While quite a large body of research has reported
on the psychological and physiological consequences sexual objectification and self-
objectification can have on women (Frederickson & Roberts, 1997), research on the effects of
observing and interacting with sexually objectified women or media images has been rather
sparse in comparison (Heflick et al., 2010). In outlining objectification theory and self-
objectification, Frederickson & Roberts (1997) named several effects including stress, anxiety,
47
loss of peak motivational states, and failure to monitor essential bodily functions. These often
result in depression, eating-disorders and other mental and physical disorders. In the context of
this work, the effects self-objectification might have on women and girls will only be of minor
importance. The focus will be on examining possible effects of observing and consuming
sexually objectifying media portrayals. As the APA report (2007) states, sexual objectification of
women and girls is ubiquitous and occurs in face to face interactions as well as when consuming
media. Advertisements only showing women’s legs, cutting off their heads, or depicting them as
objects are commonplace in today’s media. However, women do not necessarily have to be
naked or even sparsely dressed to be sexually objectified. Gervais et al. (2012) conducted a
series of experiments investigating whether women’s bodies are reduced to their sexual body
parts as compared to perceived as a complete person. The stimulus material for the study
consisted of images of men’s and women’s chest and waist area. The men and women in the
pictures were presented wearing clothing. Chests and waists serve as indicators for biological sex
and have been found to be sexually objectified for both men and women. After presenting
participants with images of men and women, attached to a whole person or disconnected, the
researchers concluded that while men’s body parts were more reliably recognized in connection
to an entire body, women’s disconnected sexual body parts were equally well recognized
whether presented in connection to an entire body or isolated (Gervais et al., 2012). These
results, as the authors note, provide additional evidence for the hypothesis that women’s bodies
are reduced to their sexual body parts. Relevant for categorizing certain channels and selecting
streamers for the experimental condition is that waist and chest can be seen as sexual body parts
and are sufficient for providing the necessary stimuli for such a study.
48
Reducing women to their sexual parts can have severe consequences for how we perceive
them. As a result of three studies, Vaes et al. (2011) found that participants dehumanized
sexually objectified women. Men were also sexually objectified but that did not result in
perceiving them as infrahuman. Both, male and female participants, dehumanized the sexually
objectified women. However, they did so for different reasons. According to Vaes et al. (2011),
men sexually objectify and as a consequence dehumanize women because they are sexually
attracted to them. Their focus shifts from the woman’s personality to her sexual body parts.
Women, on the other hand, did not dehumanize the sexually objectified women because of
attraction but rather because they were trying to distance themselves from them. Women judged
the objectifying portrayals as vulgar and superficial. The more women agree with these
judgments about sexually objectifying depictions of another woman, the more likely they are to
place them in an out-group, other and, as a consequence, dehumanize them (Vaes et al., 2011).
Heflick and Goldenberg (2009) conducted a series of experiments using appearance and
personality focused perceptions of Angelina Jolie and Sarah Palin. In the appearance focused
condition, participants were instructed to focus on the women’s appearance whereas participants
in the personalized condition were told to focus on the person as a whole. The study found that
merely creating an objectified version of a person through instructing people to focus on their
appearance results in people perceiving the objectified women as less moral, warm, and capable
(Heflick & Goldenberg, 2009). Following up on their results, Heflick et al. (2010) ran another set
of experiments to rule out alternative explanations for the results, such as stereotype activation.
Additional evidence for sexual objectification causing the changes in perceptions, was also
provided by looking at whether the same effects occur when participants are instructed to focus
on a men’s appearance versus person. Again, they found that when focusing on a woman’s (but
49
not a man’s) appearance, rather than their person as a whole, people perceived them to have less
humanness, i.e. they rated them less moral, less competent, and less warm (Heflick et al., 2010).
These results indicate that simply through objectifying a media representation of a woman,
people tend to rate them as less competent, moral, and warm. Nakandala et al. (2016) noted that
every popular female streamer receives objectifying comments in their chat. This would imply
that at least some percentage of Twitch viewers seems to focus on a female streamer’s
appearance rather than her person or her gameplay. As discussed earlier, many female streamers
emphasize their body and especially the sexual body parts, such as the chest area. Based on the
data available to Nakandala et al. (2016) it is impossible to determine whether the commentary is
caused by the female streamers presenting themselves in a way that makes them more likely to
be objectified or whether the viewers just feel like they have the right do so. And while the ‘boob
streamer’ stereotype implies that at least some portion of female streamers choose to put an
emphasis on their body in general and their chest, it is likely that because women are often
perceived as fake-gamers and invaders that have no place in gaming culture, the male majority of
Twitch viewers focuses on their appearance rather than their ‘fake’ gameplay or their
personality.
H1a: Appearance will be ranked to be more important than personality or gaming skills
for female streamers’ success on Twitch.
H1b: Gaming skills and personality will be ranked to be more important than appearance
for male streamers’ success on Twitch.
Analog to the findings presented by Heflick and Goldenberg (2009) as well as Heflick et
al. (2010), focusing on a female streamer’s appearance should then lead to her being sexually
objectified and perceived as less moral, warm, and competent. However, since this work
50
investigates whether some female streamers are more likely to be sexually objectified due to the
way they choose to design their channel and present themselves on stream, creating appearance
versus person-focused conditions is not the goal of this work. The instructions used by Heflick
and Goldenberg (2009) to create their experimental conditions, however, will still be used to
create an objectified versus a personalized condition. Nevertheless, explicating the relationship
inquired by research question RQ2 on whether certain female streamers on Twitch are likely to
be objectified, hypotheses H2a and H2b anticipate the following relationships:
H2a: Viewers will rate an objectifying representation of a female streamer as less skillful
at the game(s) she is playing on stream compared to ratings of a personalized portrayal.
H2b: Viewers will rate the personality and gaming skills of an objectifying representation
of a female streamer as less important for success compared to ratings of a personalized
portrayal.
Gaming skills and success criteria are very specific to Twitch, gaming culture, and the
practice of streaming. Since it will also be interesting to see if these perceptions go beyond the
task at hand, the next hypothesis will address the general aspects of humanness expected to be
impacted by sexual objectification.
H2c: Viewers will rate an objectifying representation of a female streamer as less warm,
moral, and competent compared to ratings of a personalized portrayal.
Since Twitch is not necessarily like television where people often watch a whole show
but rather tune in and out of channels, watch channels simultaneously or follow their friends onto
a stream they are watching at the moment, it is likely that a viewer will probably stumble from
one female streamer’s channel onto another. As the Reddit-post quoted earlier demonstrates,
51
many female players and streamers are worried that a ‘boob streamer’s’ negative image could
influence expectations viewers have towards them. Schooler (2015) conducted an experiment
investigating the effects of presenting an objectifying media portrayal of a woman alongside a
positive female role model. She found that participants, who viewed an objectifying
advertisement presented next to an article about a positive and strong female figure, in this case
the university’s student government’s president, rated the positive role model as less competent.
These findings indicate that the possibly positive effects of consuming media featuring portrayals
of strong and competent women could be mitigated by presenting an objectifying advertisement
alongside (Schooler, 2015). Women in esports are rare but they exist and some of them stream
on Twitch. However, switching over from a female streamer viewers had just sexually
objectified to a highly skilled female player could lead to perceiving the highly skilled gamer as
less competent. Therefore, the next hypotheses aim to investigate possible effects of alongside
representation:
H3a: Viewers presented with a sexually objectifying image of a female streamer
alongside an image of a female esports competitor will rate the esports athlete as less likely to
succeed.
H3b: Viewers presented with a sexually objectifying image of a female streamer
alongside an image of a female esports competitor will rate the esports athlete as less competent,
warm, and moral.
Reinforcing Stereotypes and Cultivating Sexist Opinions?
In video games, women face a multitude of sexist stereotypes. The majority of these was
likely born out of the general assumption that gaming is inherently male and women do not
belong in these spaces. Common stereotypes are that all women are bad at gaming, they only
52
game because they seek attention from male players or play because it is their boyfriends’ hobby
(Fox & Tang, 2014). Nakandala et al. (2016) noted that while not all viewers are harassers, all
popular female streamers are the target of harassment and objectifying comments. Therefore, this
issue appears to be pervasive on Twitch. The results of comparing chat messages for male and
female streamers also imply that viewers tuning into Twitch will most likely be confronted with
recurring patterns. Evidence for cultivation effects from gaming have been a controversial issue
with several studies finding opposing results. A longitudinal survey conducted by Breuer et al.
(2015) found no change in general sexist attitudes for long-term gamers. Another survey-based
study published in 2015 found playing games with sexist content to be related to rape myth
acceptance modeled via hostile sexism (Fox & Potocki, 2015). However, neither of these studies
can claim causal effects or the lack thereof due to the survey-based research designs. Chong et al.
(2012) examined cultivation effects from playing a violent video game. They found evidence for
first order cultivation effects but failed to detect any second-order cultivation effects. Cultivation
theory originally described effects of viewing recurring patterns on television that, especially for
heavy users of this medium, came to impact certain believes and attitudes about the real world
(Gerbner & Gross, 1976). First order cultivation effects refer to general beliefs about our world
usually explicated as numbers or percentages. Examples for first order cultivation effects entail
which percentage of the population television viewers assume to be doctors or working in law
enforcement since these can be related to recurring patterns in television programming. Second
order cultivation effects refer to changes in attitudes, such as whether viewers hold more
negative attitudes towards women because they are often portrayed as damsels in distress (Chong
et al. 2012; Mierlo & Van den Bulck, 2004). Chong et al. (2012) found first order cultivation
effects for the percentage of deaths resulting from car accidents and drug overdoses. Participants
53
playing 12 hours of a violent video game called Grand Theft Auto: Liberty City significantly
overestimated these numbers compared to a control group not playing the game. These effects
were to be expected after previous research found that some cultivation effects from video
gaming may occur. However, video game cultivation effects are more likely to occur for
elements of the game that can directly be related, or ‘mapped’ to real life experiences and events
(Williams, 2006; 2010). The game Chong et al. (2012) chose features high speed car chases and
drug abuse so it can be assumed that these events from the game world are likely to be mapped
to real world scenarios.
Twitch can be understand as a window into gaming culture but, as a medium consumed
quite heavily by some viewers, it could also have an influence on real world perceptions. Women
are still seen as a rarity in gaming, for some games and game genres rightfully so. While the
gender split for overall video gaming (including mobile and social gaming) might be around
50%, for many online PC and console games, the numbers appear to be more heavily skewed
towards male players. In 2012, Riot Games, developer and publisher of one of the most popular
online and esports games League of Legends released an infographic indicating that more than
90% of their player base is male (IGN, 2012). Yee (2017) reported that they found gender splits
around 85% male to 15% female for League of Legends as part of their survey on player
motivations. Watching more female streamers playing video games on Twitch could have an
influence on whether viewers perceive female gamers to be more common, more normal. The
platform could provide a degree of visibility for female gamers that counteracts the common
misperception that women do not play video games at all or do not play them competitively.
Since the present work will employ a research design that uses streamers mainly playing League
54
of Legend a more specific hypothesis will capture the relationship between watching League of
Legends streamers specifically and beliefs about the player base of this game.
H4a: Regular viewers of female streamers will estimate the percentage of female gamers
to be higher.
H4b: Regular viewers of female streamers will estimate the percentage of female esports
competitors to be higher.
H4c: Regular viewers of female League of Legend streamers will estimate the percentage
of female League of Legends players to be higher.
However, being repeatedly confronted with female gamers streaming with a large camera image,
sexually objectifying them, and perceiving them as less skillful at gaming could also negatively
affect viewers’ attitudes towards female gamers in or women in general. Playing games that
entail violence and sexualized portrayals of female characters has been found to be associated
with a stronger belief certain conservative sex roles are appropriate (Dill & Thill, 2007). The last
two hypotheses investigate possible effects viewing and objectifying female streamers could
have on sexist attitudes towards female gamers as well as on hostile and benevolent sexist
attitudes towards women in general.
Since positive media portrayals can also have positive effects on acceptance of
minorities, e.g. through vicariously experiencing contact (Schiappa, Gregg & Hewes, 2005), the
effects are not merely measured in comparison to a control but in comparison to viewing a
female streamer that is less likely to be objectified due to the focus of her content.
55
H5a: Viewers who regularly watch sexually objectifying portrayals of female streamers
will have more explicit sexist attitudes towards female gamers than viewers of personalized
portrayals.
H5bc: Viewers who regularly watch sexually objectifying portrayals of female streamers
will have more explicit sexist attitudes towards women in general than viewers of personalized
portrayals.
Additional Considerations and Theoretical Frames
In addition to the theories and previous research leading to the hypotheses guiding this
study, other theoretical concepts have been helpful in framing the thought processes leading up
to the presented hypotheses and the criteria employed to categorize channels. They will again be
employed to help interpret the results in chapter five. Performing ‘Female’ Gender on Twitch
Mastering technology and video games has a long tradition of serving as display of
masculinity (Dunbar-Hester, 2008; Taylor, 2012; Shaw, 2014). Since the stereotypical gamer is
often still perceived as male and gaming culture is seen as a male-dominated space, performing
acts that constitute masculinity can be expected to occur quite frequently on a live streaming
platform like Twitch. For example, male gender constituting acts can entail behaviors, such as
cursing, yelling, burping, or making crude jokes. In Wotanis and McMillan’s (2014) in-depth
study of a female YouTube content creator, the authors found that the female YouTube star often
engages in implicitly male performing acts. They describe her raising her voice and using foul
language as ways to establish a ‘tough boy’ image. Creating an image that conveys a certain
resilience and toughness might very well be necessary to fit into the culture on YouTube and,
presumably, Twitch. As mentioned earlier, Burgess and Green (2009) note that sexist and
abusive comments are perceived as a normal part of YouTube culture. Wotanis and McMillan
56
(2014) suggest that one possible explanation for the implicit performance of masculine gender
acts while looking hyperfeminine might very well be a strategy of navigating this generally
hostile and sexist culture. The YouTube star and subject of Wotanis and McMillan’s (2014)
research always wears make up and low cut tops. Her videos usually feature a camera angle
showing her upper body in full and from a slightly elevated angle. Female twitch streamers and
YouTubers seem to constantly be performing a rather confusing balancing act between
masculine and feminine acts of gender performance. According to Butler (1990) it is the sum of
a plethora of these acts that, in the end, constitute gender. Note that these acts can very well be
contextual and fluid and, as the term implies, they are also performative. While sometimes
implicit and not intentional, they are still part of a performance of a role that individuals play in a
certain space or community. Male and female streamers alike curse, get drunk on stream and
yell at failing teammates. However, most male streamers wear a t-shirt or a hoodie while female
streamers can often be seen wearing make-up and styled hair.
The ‘Cool Girl Trap’ and the illusion of choice. “Being the Cool Girl means I am a
hot, brilliant, funny woman who adores football, poker, dirty jokes, and burping, who plays
video games, drinks cheap beer, loves threesomes and anal sex, and jams hot dogs and
hamburgers into her mouth like she’s hosting the world’s biggest culinary gang bang while
somehow maintaining a size 2, because Cool Girls are above all hot. Hot and understanding.
Cool Girls never get angry; they only smile in a chagrined, loving manner and let their men do
whatever they want. Go ahead, shit on me, I don’t mind, I’m the Cool Girl.” This description of
the cool girl, a trope identified throughout popular narratives by several academic and non-
academic authors, here expertly explained by Gillian Flynn in her novel Gone Girl, could very
well serve as a characterization of several streamers hosting Twitch channels on a Saturday
57
night. It reflects the balancing act between appearing very feminine, adhering to current beauty
standards and performing male acts of gender performance, such as drinking, burping and
cursing. The interesting part about the cool girl trope as described by Flynn (2012) is that the
main protagonist as well as several authors writing for popular media outlets agree that she is a
myth. As Osterndorf (2015) puts it in a piece published on the Daily Dot, the cool girl is an act
that is completely fabricated to appeal to men. Bim Adewunmi (2012) further elaborates on the
cool girl as the girl who claims to just get along better with guys and is not up for any of the
usual ‘bitchiness’ most women are suspected to exhibit. Adewunmi (2012) also brings up an
essential dilemma that women who just love playing video games or watching football –
traditionally seen as straight men pastimes – constantly face. Some men do not accept women
entering their domain. They call them out for being fake fans, not real gamers or simply
“attention whores” because seeking men’s attention is obviously the most likely explanation for
women pursuing hobbies that are generally labeled as male.
It seems that performing as cool girls is one of the strategies female gamers choose for
gaining access to gaming culture and spaces. Shrugging off the harassment and laughing about
jokes that are not funny secures them a spot in the in-group. It also allows them to retain the
ability to perform female gender acts which women who choose to, for example, mask their
gender and hide their femininity lose. However, as discussed earlier, the cool girl does not really
exist. It is as much an illusion as the illusion of choice for women to not having to put on an act
in order to gain access. Female streamers are in total control of their own channel
6.
They create
the content they broadcast. They can choose how to design their profile and which clothes to
wear on stream. They can ban whoever they want from their chat and turn off the stream
6
With the obvious restrictions outlined in the code of conduct Twitch enforces.
58
whenever they feel like it. In theory, female streamers have all the power to broadcast the kind of
content they like. However, as discussed in more detail in the section on sexual objectification
and stereotypes, many viewers will probably not assume them to be skillful gamers. Therefore, it
is unlikely that viewers will watch the stream to satisfy cognitive needs for information and
learning about the game. We also know that female esports competitors and high-level players
are still rare and many viewers come to watch their idols play outside of tournaments. Remains
the option to provide entertainment and a great community. After all, social integrative motives
are the best predictors for subscription behavior. 75% of the viewers on the platform (only a year
ago, Roose (2016) claimed a ratio of 95% male to 5% female) are still male and might therefore
look for a streamer and a community that they can identify with. While there is no data to back
this claim up, qualitative research, such as Hamilton et al.’s (2014) study on channel
communities, suggests that distinctive channel communities are heavily influence by the
streamers themselves. Ruling out these two types of concepts for streams which might attract a
certain type of viewer or satisfy a certain type of viewer’s demand, female streamers are not left
with too many options. They are, however, still one of few women on a platform with mainly
populated with male users.
To a certain degree, the cool girl trope also resonates with a phenomenon characterized
by Levy (2005) as the emergence of the ‘Female Chauvinist Pig’. This term refers to women
choosing to actively and willingly embrace our sexualized society. Whether it is participating in
shows like Girls Gone Wild or choosing pole dance over other forms of exercising. However,
Levy (2005) argues that this post-feminist notion of choice is merely an illusion of choice –
caught in the cool girl trap. It is impossible to tell whether female streamers actively make a
choice to create the kind of brand they want to represent or whether they choose the strategies
59
left to them. However, it seems that for many female streamers seeking to be successful on
Twitch, the choice to not adhere to the ‘boob streamer’ stereotype might only be an illusion. A
lack of choice for which they are often heavily judged. And even if female streamers choose to
not give into pressure imposed on them by societal ideals and financial opportunities, Nakandala
et al.’s (2016) analysis found that most of them will still receive sexist and abusive commentary.
Summary of Chapter Two
Twitch, from a structural perspective, incentivizes having many concurrent viewers
through placing those streams on the top left of the page. An interesting thumbnail and title can
bring new viewers to your channel. Imitating strategies that have proven successful for more
popular streamers can help to generate a larger following. Despite popular opinion that women
are taking over Twitch, female streamers as well as female gamers playing many popular PC
games are still a minority. Oftentimes, they are met with sexism and hostility framing them as
attention seeking fake gamers. Imitating popular female streamers, many women on Twitch
choose to put a lot of emphasis on their body rather than their gameplay. It is likely that because
women are perceived as an out-group in the gaming community and streamers present
themselves as hyperfeminine, focusing on their bodies, many viewers sexually objectify female
streamers. Possible effects could include rating female streamers as worse gamers and
reinforcing and exacerbating common sexist stereotypes of female gamers and women in
general.
60
Chapter Three: Methodology – Observations and Experimental Design
To test the presented hypotheses, an experiment consisting of an initial survey, a four-
week viewing phase and two post surveys was conducted. As the independent variable
manipulated for the experiment, viewers were randomly assigned to watch one of three streams.
These streams were selected based on a series of criteria ranging from channel design to skill
level at the game being played. Several potential channels were then pre-tested on a small sample
of Twitch viewers to ensure the intended manipulation was successful. The process can be
described in eight steps:
1. Observations and literature review leading to potential channels for each condition and
the control.
2. Pretesting of potential channels and final selection-decision.
3. Participant recruitment via different social media outlets.
4. Screening survey including information sheet.
5. Initial survey and random assignment to conditions.
6. Four-week viewing phase.
7. Second survey
8. Follow-up survey
The first section of this chapter will describe the process for categorizing streamers and selecting
potential channels that viewers could be assigned to watch. From there on, the chapter will
follow the usual format and begin by describing participants, followed by operationalization and
measures, give a detailed description of the experimental manipulation, and mention any
noteworthy events possibly impacting the results.
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Selecting Channels for the Experimental Manipulation
The first participatory observations leading up to the present work started in early 2014.
It was during the first phase of data collection for a qualitative analysis of Twitch channels that
Twitch decided to make changes to their code of conduct. The community started complaining
about a new trend of Twitch streamers using the platform to stream themselves engaging in other
activities than gaming. Twitch decided to enforce a more rigid ruleset in regard to showing
nudity and content unrelated to games. Male and female streamers alike are obliged to cover
their chest at all times when on stream. Undergarments as well as naked feet were banned as
well. In addition, streamers were asked to provide a certain amount of gaming-related content
when broadcasting under one of the gaming categories
7
. The first exploration of Twitch channels
by the author (Uszkoreit, 2015) involved comparing how successful male and female streamers
design their channels, what activities besides playing the game their stream was listed under they
chose to show on stream, what they were wearing, and which information they chose to provide
in their profiles. The analysis found several differences in the observed channels hosted by
female and male streamers. Female streamers’ webcam feed was larger in comparison to the
gaming screen, they always wore make up and styled their hair, many streamers gave a lot of
personal information on their profiles and used pink and light colors for their profiles. Male
streamers’ channels featured smaller camera images, often only brief descriptions of their
hardware on their profile and the hosts were mostly fully clothed in t-shirts and hoodies. While
the male streamers were playing the game their stream was listed under, several female streamers
doodled or had their viewers donate to make them do exercises on stream (Uszkoreit, 2015).
7
Since 2014 the Rules of Conduct (Twitch, 2017) have been updated repeatedly and these restrictions do
not apply anymore. This is mostly due to the addition of IRL and Creative categories that do not require
broadcasters to feature gaming-related content.
62
Based on this analysis, several years of observing and participating in stream chats and
combining the findings with results from previous research examining sexual objectification,
gender performance, and sexism on YouTube, several criteria were identified as relevant for
categorizing streams. Please note that this analysis is not a representative content analysis and
does not claim to be generalizable to all of Twitch. However, as explained in the second chapter,
Twitch’s platform operates under the “the rich are getting richer” principle which encourages
less successful streamers to imitate strategies of the more successful streamers which were the
basis for the 2014 analysis by the author. Over the last few years of the platform’s existence, it is
likely that many Twitch viewers have adopted heuristics that allow them to quickly categorize a
stream so that they can make a decision on whether it sparks their interest or not.
Stream size. The size or rather the popularity of a stream is not necessarily easy to
assess. Follower numbers and total views are displayed on the channel page. However, the
number of subscribers is not publicly accessible. One of the perks of subscribing to a channel are
emoticons provided by the streamer that can be used globally on Twitch. Based on the number of
emoticons a channel offers for subscribing, a rough estimate of subscriber numbers can be made.
For example, a channel that has less than 100 subscribers will only receive up to 9 emoticon
slots. Having more than 20 emoticons indicates that a channel has more than 500 monthly
subscribers. The larger the community and the larger the concurrent viewer numbers are, the
more active the chat will likely be. Hamilton et al. (2014) noted that there is a saturation point of
a channel somewhat outgrowing its community. When the chat moves to fast to have a
conversation and the community becomes too large for members to know the regulars, social
demands are likely to become less important for watching the channel. It is likely that channels
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with more than 5,000 concurrent viewers and an active chat either attract viewers for the
gameplay or for the streamer but not necessarily for the social interaction between viewers.
Corporate stream versus private stream. Some channels are hosted by esports leagues
or game publishers rather than one personal streamer. Obviously, these tournament streams do
not usually have a dedicated community. While the chat might be used to have a conversation in
reference to the gameplay, during most tournament streams the chat scrolls quite fast. Most
likely, viewers watch these channels for the gameplay and the entertainment rather than
interacting with other viewers. Casters of tournaments do not react to chat so it is more similar to
live tweeting during a conventional sports game with no direct feedback from the content
creators.
Game choices. While some streamers have established themselves as experts on a
specific game or genre with the respective game being added to the glue that keeps the channel
community together, others are ‘variety streamers’. Whether a channel is hosted by a gamer
dedicated to one game and probably playing it on a high level or a streamer dabbling in many
games will likely have an impact on why viewers tune in. If viewers’ motivation is to learn more
about a specific game and keep up to date with current strategies they will seek out an expert
streamer. While variety streamers might attract viewers looking for new games to play, their
communities will be centered around the streamer rather than the game or the streamer’s status
within the game. Despite this criterion being a little more difficult to pin down, it mostly feeds
into the distinction between gameplay focus and streamer focus. While it is probably harder to
establish a sizeable following as a variety streamer in the first place, channels only dedicated to
one specific game cannot reach the same degree of popularity as a variety streamer.
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Size and angle of the camera image. Many successful Twitch broadcasters stream with
a camera. While some male streamers choose to only do voice commentary, the majority of
female streamers seen in popular game categories, such as League of Legends or Counter Strike:
Global Offensive, stream with a camera. Major differences were observed in the portion the
camera feed takes up of the broadcasted stream. Depending on which game is streamed, the
positioning of the camera can be quite tricky. If the goal is to provide content that allows viewers
to follow the gameplay as closely as possible and provide all the information the player has, the
camera cannot block important game interface elements. A smaller webcam feed therefore
signals a focus on the gameplay rather than the streamer. In addition to size and position of the
webcam feed, it is also important to take into consideration how closely the camera zooms in on
the streamer, how it is angled and what can be seen in frame. Is the whole frame taken up by the
face or is the top of the head cut off but viewers can see the chest area? Drawing on the findings
presented by Vaes et al. (2011) and other studies, this is likely to have an impact on whether
viewers perceive the streamer as a whole person or rather focus on sexual body parts. Framing
through camera distance and angle can therefore be an indicator for whether a channel focuses
on the streamer as a person, i.e. their face is usually centered in frame and viewers can maybe
see objects in the background that allow them to get a glimpse into this person’s life. The other
pole of this dimension would be a focus on the streamer’s body and mainly their sexual body
parts.
Streamers’ appearance. A streamer’s personality and the regulars in their channel
contribute to shaping the channel’s community (Hamilton et al., 2014). Obviously, the way a
streamer looks on camera has an influence on not only which kind of viewers they attract but
also what these viewers will expect. The top female and male streamers in different game
65
categories observed by the author in 2014 were all young adults, likely in their twenties. While
the men’s clothing can be described as very casual, t-shirts and sweatshirts or hoodies, the
observed female streamers all seemed to have invested more time into dressing up, styling their
hair and putting on make-up (Uszkoreit, 2015). The impressions were very similar to how
Wotanis and McMillan’s (2014) described the way the famous popular YouTube star chose to
present herself in her clips. It is important to note, however, that a YouTube video is a recording
and therefore a much more controlled performance. While many professional streamers are live
almost every day, YouTube content creators do not necessarily create videos on a daily basis.
Therefore, the level of preparation some female streamers undergo almost every day are a major
time investment. During the time frame in which several streamers were selected to be pretested
for the experimental manipulation, one of the potential streamers decided to quit streaming. One
of the reasons she mentioned for quitting was that preparing to stream each day took her 30-40
minutes just to apply make-up. Even more time was spent on dressing up and putting on her
extremely uncomfortable push-up bra (Hernandez, 2016). Live streaming, especially for women
on Twitch, is performing. Emphasizing sexual body parts by wearing certain types of clothes
puts a focus on the streamer’s appearance and might take away attention from the gameplay. Of
course, not all observed female streamers dress in low cut tops but most popular female
streamers still use make-up and obviously put more time in optimizing their appearance than
male streamers. Again, this is not surprising given what is expected of women in our society no
matter where they work or appear. However, it is also true that we watch these women play
video games in their homes which is generally neither the place nor the activity that women dress
up for. It is also not surprising that women are judged by the way they look and what they wear.
So even if a female streamer is not actively trying to promote her physical appearance over
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gameplay and personality, evidence from other context implies their appearance may still be
perceived as more relevant. A streamer’s appearance will also play into the dimension between
focus on gameplay versus streamer and then, as a subcategory of that, the focus on a streamer’s
personality versus physical appearance.
Channel design. The channel design includes banner and avatar images, the static
channel profile page, its design and its content, as well as stream overlays. Not all Twitch users,
including streamers, use a photo of themselves as their avatar. Many more popular streamers
have created logos for their brand and use their logo as their avatar. Obviously, a logo is less
gender-specific than a photograph and puts more emphasis on the channel’s brand rather than the
streamer as a person. Once a stream has reached a certain level of popularity it is common to be
sponsored by gaming-related brands and using a recognizable design theme, a brand identity if
you will. Being sponsored or part of a well-known gaming organization can add to a channel’s
reputation. Newcomers to the channel are more likely to perceive it as a legitimate channel
providing quality gameplay. In her first observations, the author found that popular male
streamer’s profile pages usually included information about their sponsors and their computer
specifications. Profiles often used darker colors. The popular female streamers channels’, on the
other hand, featured lighter colors, more personal information, such as age, ethnicity, hobbies,
and favorite foods. In addition, a lot of space was dedicated to describing subscriber perks and
honoring the top contributors to the stream (Uszkoreit, 2015). Again, channel profiles providing
information about computer specifications, sponsors, and games being played indicate a focus on
gameplay. The same applies to simplistic game overlays highlighting the importance of the
ability to easily follow the gameplay. More personal information, announcement of community
events, such as viewer gaming and an emphasis of channel supporters indicate a focus on the
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streamer as well as the community. Streaming overlays that rank the visibility of the latest
donations or honoring the most recent subscribers higher than being able to see all elements of
the streamed game also speak to an emphasis on the streamer and the community rather than the
gameplay.
Chat interaction, subscriber and donor rewards. The level of interaction the streamer
has with the chat and how much attention subscribers and donors receive is likely to impact on
the kind of community and culture a channel fosters. For very popular streams, chat usually
moves so fast and resubscription alerts pop up every few minutes. If a streamer this popular
decides to focus on interacting with viewers and contributors, they are likely to never really get
to play the game they are streaming. Many female streamers who have built their brand and
success on rewarding contributors with attention and spending a lot of time interacting with chat
likely find themselves in a bit of a conundrum the more successful their stream becomes since
they do not find the time to play a game without constantly pausing to interact with their viewers.
Depending on the motivations that bring viewers to a stream this behavior often results in people
complaining about the lack of gameplay. If the focus of a channel is on the gameplay, viewers do
not expect the streamer to pause playing and read out a donation to reward the contributor with
attention. However, if the focus is on the streamer and the interactions the streamer has with
viewers and the community in general, an interruption of the gameplay will be commonplace and
probably expected by contributors. Some of the observed female streamers also have quite
elaborate subscriber and donation alerts that will play a music loop and a video or gif in the
middle of the screen rewarding the contributor with five seconds of fame. For female streamers,
it can be quite important to reward viewers and contributors with attention since personal
integrative motives could be influencing their viewing choices.
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Different Types of Channels and the Motivations to Watch Them
In sum, the following dimensions can be deemed useful when categorizing Twitch
streams:
• Private streamer vs. corporate channel (e.g. an esports league’s channel)
• Popularity or size (ranging from smaller non-partnered channels to popular Twitch-
partner streams)
• Variety gamer vs. expert in one game or genre
• Focus on gameplay vs. focus on streamer
o Focus on personality vs. focus on appearance
o Level of interaction (frequent vs. infrequent) between
Viewers with viewers as well as viewers with streamer
Mostly viewers with streamer
Based on findings by Hamilton et al. (2014) as well as Sjöblom and Hamari (2016) it can be
assumed that different motivations will play a role in which channels viewers join and return to.
It is likely that cognitive motives as well as a demand for entertainment draw viewers to a
popular expert streamer focusing on gameplay. If viewers want to socialize and relax they will
probably tune into a variety streamer’s channel they have been watching for a few months now
and have a chat with regulars and streamer. These criteria will be applied to describe the selected
channels and explain the decision-making process. They will also serve as a categorization to
explain differences in motivations to tune into the assigned channels.
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Method
After an unconventional start to this chapter, outlining the criteria used to categorize and
identify adequate channels to achieve the desired experimental manipulation, the remaining
subsections will describe the method in a more conventional format.
Participants. Whether the selected channels were perceived as intended was first tested
in a pretest with 30 participants. These participants were recruited through the mailing list of the
University of California at Irvine’s (UCI) Esports Arena. The inclusion criteria for pretest
participants were similar to the criteria for recruiting participants for the experiment. Given that
the interpretation of certain cues such as clothing and styling might be heavily influenced by
cultural upbringing, only participants living in Canada and the Unites States were recruited.
Another criterion for inclusion was that participants had to be native English speakers or at least
be fluent in English to rule out possible issues arising from language barriers. Only adults were
recruited to participate since some Twitch channels stream inappropriate content for minors and
participants were also awarded a compensation. Pretest respondents were rewarded with a few
hours of free gameplay in the Esports Arena at UCI. Experiment participants were compensated
with $15 (20 CAD respectively) Amazon.com and Amazon.ca gift cards. In addition, each
participant responding to all survey waves was entered for a raffle of three $100 gift cards for
Amazon.com. The final inclusion criteria were being registered on Twitch and using the platform
regularly (at least several times per month) and playing online games on the PC or console.
These criteria are important because the experiment did not intend to examine reactions and
effects of watching Twitch on newcomers to the platform. Without prior knowledge of gaming
culture and the platform, knowledge of stereotypes and certain codes and cues could not be
70
assumed. The screening survey also included an item inquiring whether participants would
generally be available during the regular streaming hours of selected channels.
A total of 105 participants ended up finishing the four weeks of watching their assigned
channels and all three survey waves. 80 of them self-identified as male and 24 as female, one
participant identified as gender variant or non-conforming. After the third survey wave, response
rates varied significantly across assigned conditions. The next subsection on sampling
procedures will discuss this in more detail.
Condition 1 (objectifying). Out of the 29 respondents assigned to this condition, 20%
(six) identified as female, 80%. 20 participants identified as white, one as black, four as Asian,
and four as Latinx. 25 were living in the United States and four in Canada. 9 stated to have
finished high school, 15 completed ‘some college’ and four earned a Bachelor’s degree. Only
one participant earned a Master’s degree. Participants in this condition, on average, joined
Twitch 3.8 years ago and are aged 18 through 35 with a mean of 22.5 years. On average,
participants in this condition were slightly younger than respondents assigned to the other
experimental condition and the control. However, this is mostly due to two outliers slightly
skewing the mean age in the other two conditions.
Condition 2 (personalized). In total, 38 respondents assigned to this condition completed
all three survey waves. Gender ratio and age were similar to the first condition. Age ranged from
18 to 42 with a mean of 24 years. 26% identified as female and the remaining 84% as male. 24
people described their race/ethnicity as white, one as black, six as Asian, four as Latinx and four
participants self-described as mixed race. 33 of the respondents live in the United States and 5 in
Canada. Seven finished high school, 22 stated to have done ‘some college’, 7 received a
71
Bachelor’s degree and one stated to be completing advanced graduate work or a PhD. On
average, they joined Twitch 3.5 years ago.
Control. Just like in the personalized condition, 38 participants finished all three survey
waves. Participants’ age ranged from 18-50 years with an even slightly higher mean of 25.6
years, likely due to the slightly older maximum age. Eight participants self-identified as female,
29 as male and one as non-conforming. In this group, most participants labeled themselves white
(27), two chose to describe themselves as black, one as American native, one as Asian, two as
Latinx and five as mixed race. Again, 33 of them live in the United States and five in Canada.
Most participants completed high school (11) or some college (16), nine stated to have a
Bachelor’s degree and one earned a Master’s degree. Again, the mean length of Twitch use was
3.5 years.
Pretest participants. Out of the 30 participants responding to the online survey eight were
from Canada and 22 from the United States. Only four identified as female (15%) and 26 as
male. Participant age ranged from 18 to 35 with a mean age of 22.7 years. They had been
registered Twitch users for an average of 3.5 years and most people frequently watched MOBAs
and FPS on Twitch as well as some RPGs. These were also the most played genres. Pretest
participant demographics and gaming habits reflect the general population of Twitch users quite
well.
Sampling procedures. The pretest sample was mainly recruited with support of the UCI
Esports Arena staff. This was a handy solution which allowed the researcher to not tap into her
gaming networks and social media channels and possibly spoiling potential participants for the
actual experiment. The goal was to keep the samples separated to avoid undesired contamination
effects. Mainly three channels were used to recruit for the experiment: Twitter, Reddit, and
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Discord
8
. A small recruitment message along with a link to the screening survey was tweeted
and retweeted by several major Twitch streamers, posted on the Twitch-specific subreddit, and
linked on several Discord servers. Participants self-selected themselves by clicking on the link to
the screening survey. This screening survey included screening questions for the inclusion
criteria described above and the information sheet. Recruitment process, compensation, research
design, information sheet and debrief were approved by the Institutional Review Board of the
University of Southern California. Over 800 people started the screening survey that asked for a
valid email address and their Twitch user name. Only 250 responses that included email
addresses and Twitch names were recorded after screening each potential participant. Due to the
low response rates, the compensation was raised from an initial $10 per participant and three $50
gift cards to the described $15 and three $100 gift cards
9
. Only a few days after the screening
process was finalized, emails with the survey link were sent to 250 participants. This first survey
asked for demographics and some general information on gaming and Twitch viewing habits. It
concluded in assigning participants randomly to the experimental conditions or the control. 165
participants completed the survey and were assigned to their channels (55 per group). Five weeks
later, only 111 participants completed the second survey wave after the four-week long viewing
phase. Only 31 of the 55 respondents assigned to condition 1 completed the second survey. In
condition 2 and the control, 40 participants completed the second survey. The final survey,
distributed to the participants who completed the second survey within a week of recording their
response, resulted in 106 responses. As stated in the recruitment material and information sheet,
all 106 respondents completing all three survey waves received a $15 Amazon gift card and three
8
Discord is a free voice and text chat application for gamers that has been adopted by many gaming
communities. According to Venturebeat, the application has over 45 million registered users (Takahashi, 2017).
9
In agreement with the University of Southern California’s Institutional Review Board.
73
lucky winners were awarded a $100 gift card in addition. An online random name picker tool
was used to blindly pick the three raffle winners. At the end of the final survey, participants were
also debriefed about the experimental manipulation and offered the option to withdraw from the
study. All 106 participants confirmed their agreement to volunteer for this study.
Sample size, power, and precision. The total sample size this study aimed for was 150-
200 participants (50-65 per condition). The low overall response rates and a drop-off between
every survey wave, especially for participants in condition 1 significantly impacted on the
expected power and precision. The pretest resulted in highly significant results with effect sizes
of r = 0.5 and higher for hypotheses sets H1 and H2. Expected cultivation effects, however, can
be very small and despite extended efforts, such as increasing compensation and keeping
recruitment open for longer than expected, the resulting sample size was likely not large enough
to detect small effects. For medium effect sizes, r = .25, the expected power (1- β error
probability) is .82 and for smaller effect sizes, r ~ .1, the expected power for the recruited sample
size is only .6. Therefore, the absence of a measured effect cannot conclusively reject the
alternative hypotheses.
Research design. The study employed an experimental design with three survey waves,
one before and two after a four-week viewing phase. It did not use a pretest-posttest design for
all outcome variables because the first survey prior to the viewing phase deliberately avoided to
broach the topic of sexism and to disclose that the manipulation entailed viewing different
female streamers. Sexism is a very sensitive topic in gaming, especially after the infamous
#Gamergate controversy which drew the general public’s attention to how white and male-
dominated gaming culture still is. It is likely that a certain percentage of respondents might have
tried to purposefully boycott a survey examining their sexist attitudes towards female streamers
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and gamers by providing false responses. Therefore, only general measures about viewing
motivation and opinions on what criteria make streamers successful as well as demographics and
viewing and gaming habits were assessed as part of the first survey. Participants who completed
the first survey were randomly assigned to two experimental conditions and one control group
using the online survey software Qualtrics. The survey tool allows for presenting only a subset of
randomly selected people with a certain survey element while keeping track of the count. The
two experimental conditions were instructed to view different female streamers and the control
was instructed to watch a channel streaming esports tournaments of the same game. All
respondents were asked to not talk about the experiment or post on social media to avoid any
contamination between the groups. However, it is possible that due to the popularity of the
channel selected for the control some participants assigned to the control condition also watched
some of the tournament. Participants in all groups were instructed to watch their assigned
channels for three to five hours per week over the course of four weeks. Obviously, the goal was
to watch the live stream for as many of the assigned hours as possible each week. However, five
hours is quite the time commitment and because the two selected female streamers do not have a
fixed streaming schedule, participants were asked to watch recordings (‘VODs’, short for video
on demand) in case they were unable to tune into the channel while it was live. The channels
participants were randomly assigned to watch were not intentionally manipulated for the purpose
of this experiment but selected based on criteria described in the first section of this chapter.
Employing this naturalistic design aimed to yield an increased external validity as opposed to a
laboratory study.
The second survey, distributed right after the four weeks of viewing the assigned
channels asked participants to rate which criteria they believe to make the stream successful,
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which motives they thought attracted other viewers to the channel, and how accurately a set of
ten adjectives describes their streamer to assess whether they sexually objectified them.
Respondents were also presented with two sets of items measuring benevolent and hostile sexist
attitudes towards women in general and sexist opinions about female gamers. In addition, the
second survey presented participants with a screenshot of their streamer and an image of an
alleged female esports competitor. The picture was accompanied by a short invented news blurb
reporting about a player named Al1ssa being picked up by a major European League of Legends
team competing in the highest league. They were then asked to rate the likelihood of her success
and also how accurately the ten adjectives, assessing competence and warmth, describe the
athlete. Since this part of the experiment involved deception participants were debriefed about
the invented esports competitor at the end of the third and last survey.
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Figure 3. Picture of the invented esports League of Legends player Al1ssa used in the
experiment.
The final survey prompted participants to respond to questions about how skilled their
respective streamer seemed to be at gaming and whether they would tune in again. It also asked
about the same criteria for steaming success again. Finally, it debriefed participants and gave
them the option to withdraw from the study after knowing its true intent. One-way ANOVA and
t-tests were used to compare differences between groups. Paired-samples t-tests examined
differences in pretest-posttest measures grouped by experimental conditions. In addition,
regression models controlling for the influence of the assigned channel and other covariates
provided explanations for differences in outcome variables.
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Measures and covariates. The initial survey first assessed demographic variables
including gender, age, race, education, and current country of residence. Then, participants were
asked to provide information on which game genres they mostly view on Twitch and play
themselves. This information was important to control for possible differences between subjects
depending on whether they regularly view and play the game mainly played on the assigned
channels, League of Legends, or other game genres. Participants were also asked how many
hours per week they usually watch Twitch streams, how many channels they follow and how
many channels they are subscribed to. These measures were used to control for whether subjects
were heavy users or rather infrequent users since this might have an impact on their perception of
the assigned channels. In addition, an item assessed which percentage of channels they follow is
hosted by female streamers.
Criteria for streaming success. A last set of items on the first survey inquired which
criteria participants deem important for a male or a female streamer’s success on Twitch. They
were instructed to give their personal opinion on which of the following criteria matter the most:
attractiveness (divided into body, face, and general sex appeal), personality, consistent schedule,
interacting with viewers and interactions between viewers in chat, quality of presented gameplay,
choice of games, activities with viewers (i.e. playing games together with them or involving
them into raffles), channel design, and being part of a gaming organization (i.e. being a member
of a professional gaming team or a sponsored streamer with a gaming organization). Participants
were asked to rate each of the items on a five-point scale ranging from ‘not important’ to ‘very
important’. In the second survey, participants were presented with the same criteria but asked to
respond to them in regard to their respective stream (“How important would you say the
following criteria are for your assigned channel’s success?”). Then, as part of the third survey,
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they were again asked to respond to them in regard to Twitch in general. These provided pretest-
posttest measures to assess differences within subjects. The criteria for streaming success also
provided an additional measure for operationalizing sexual objectification since it can be
assumed that sexually objectifying a streamer will result in rating her attractiveness more
important than her personality or her gameplay. Responses to these items can also be understood
as part of a manipulation check.
Adjectives assessing competence and warmth. Creating measures for (sexual)
objectification was achieved through a combination of measures. The differences between
groups and within subject changes in criteria for streaming are a measure more specific to the
object of study. The ten adjectives to assess whether participants perceive the streamers as
competent, capable and warm add a more general perspective that is not merely geared towards
gaming and streaming. Schooler (2015) found differences in ratings of accuracy attributing these
ten adjectives with the strong female role presentation alongside an objectifying portrayal. As
part of the second survey, participants were asked to rate how accurately the following adjectives
a) describe their assigned streamer and then b) describe the esports competitor presented right
after an image of their streamer: competitive, competent, capable, intelligent, independent,
warm, welcoming, compassionate, likeable, and caring. All of these adjectives reflect human
qualities of competence and warmth. The perception of which, according to Heflick et al. (2010),
can be expected to be significantly reduced when a woman is sexually objectified. Participants
were asked to rate each adjective’s accuracy on a six-point scale ranging from ‘very inaccurate’
to ‘very accurate’.
Sexist opinions about streamers and gamers. Sexist attitudes towards streamers and
gamers as an outcome variable were operationalized using the sexism in video games scale
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developed by Fox and Tang (2014). The scale includes fourteen items measuring agreement on a
five-point scale. The items address common sexist stereotypes in gaming, such as female gamers
only play because they want male gamers’ attention, they are generally bad at playing games,
and usually the weakest link in a team. In addition, some of the items were presented to
participants again in a slightly modified version asking about streamers. The sexism in video
games scale as well as the modified items can be found in the appendix.
Benevolent and sexist attitudes towards women in general. Benevolent and hostile sexist
attitudes were measures using the ambivalent sexism index (‘ASI’) by Glick and Fiske (1996).
This index distinguishes between hostile sexism, which is holding actual negative attitudes
towards women and benevolent sexism which is still harmful but based on positive stereotypes
about women. While positive stereotypes are still harmful, many people holding them are not
necessarily aware of them but rather think of them as chivalry. Especially in a survey in which
social desirable responses can be expected, items measuring benevolent sexism can be expected
to provide less biased data.
Motivations to watch streams on Twitch. Motivations to watch streams were initially
measured using a simplified set of items in the first survey before the viewing phase. Participants
were asked to report their agreement with a set of questions on whether they watch streams for
entertainment, for learning about games, for watching the best players, interacting with the
stream and interacting with the community (social motives). Social-integrative and cognitive
motives as well as tension release were the motivations found to be decent predictors for how
many hours participants watched Twitch in Sjöblom and Hamari’s (2016) survey. In the second
survey, participants were then asked about what motivates other viewers to tune into the
channels they were assigned to. This time, they were asked to respond to the items Sjöblom and
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Hamari (2016) employed in their survey to investigate possible differences in what makes
different types of channels attractive to viewers.
Behavioral measures. In addition to the self-reports on surveys which also included a
‘Twitch diary’ in which participants were asked to track their own viewing schedule and indicate
whether they tuned into a live stream or watched a VOD, the chat of each assigned channel was
logged whenever they went live. Logs also included viewers leaving and joining the chat to
allow for cross checks between diaries and viewer lists. Collecting participants’ Twitch user
names also provided the option to control for whether the users followed the assigned streamer,
whether there was cross-contamination or whether users have been inaccurate in their self-
reports about which channels and how many they follow.
Experimental conditions. The goal was to keep the experimental manipulation as
natural and close to the authentic Twitch viewing experience as possible. Therefore, the decision
was made to not actually manipulate a channel but rather choose a streamer fitting the desired
criteria. A successful channel is not established over night and it was important that the
community and the streamer do not seem artificial. The streamers were not made aware of the
study participants instructed to watch them and the participants were instructed not to speak
about them volunteering for a research project. In addition, participants were not made aware of
the actual focus of the study. They were told that they will be volunteering for an experiment
investigating what makes Twitch streamers successful. Obviously, this was not completely
deceiving but rather omitting the focus on gender, objectification and sexism. To achieve the
desired variance in the independent variable, two different female streamers that already run
established channels needed to be selected. Condition 1 or the objectified condition aimed to
present participants with a female streamer corresponding to the popular ‘boobie-streamer’
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stereotype which they would likely sexually objectify. In contrast to this streamer, participants in
the personalized condition (or condition 2) were supposed to ideally not objectify the streamer at
all. At the very least, the expectation was that they would still pay more attention to her
personality, the quality of her gameplay and the community she built around her channel. Results
presented by Heflick and Goldenberg (2009) and Heflick et al. (2010) imply that sexual
objectification occurs in absence of stereotype activation. Merely instructing participants to focus
on her appearance versus her personality and actions should have already sufficed to create
variation in the independent variable. However, the goal of this study was not to demonstrate that
men and women objectify female streamers when focusing on their appearance. This has already
been established in the work of Heflick and his colleagues. The main research questions this
study addresses is a) whether viewers perceive female streamers differently based on the way
they choose to design their channels and present themselves, b) whether they sexually objectify
female streamers that choose to put a focus on their body (and their sexual body parts) rather
than their personality or gameplay, c) whether this has effects on how viewers rate their warmth,
intelligence and their capabilities at gaming, and d) whether viewing and objectifying them also
has an effect on how viewers perceive other female streamers, gamers, and esports competitors.
Based on the criteria for categorizing streamers presented in the first section of this chapter, two
channels hosted by private female streamers needed to be selected. These channels had to vary
on whether they focus on the streamer’s personality and gameplay or mainly focus on her body.
However, based on all other criteria, the streamers were supposed to be as similar as possible.
After perusing Twitch channels over the course of a few months, two potential channels for each
condition and two potential channels for the control were pretested. It is noteworthy that the two
selected streamers did not show the largest differences in regard to how they were perceived by
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pretest respondents. However, the streamer selected for the objectifying condition ended up
being the better choice in comparison to the other option because she was not too extreme in the
way she presented herself. Some of the other streamers observed had even larger camera images
and wore even less clothing. Due to ethical considerations, the names of the selected channels
were changed to fantasy names. The two selected channels were CutestSquirrel (objectified
condition or condition 1) and CandyBar (personalized condition or condition 2).
Popularity and size. Both selected streamers have entered partnership contracts with
Twitch. This indicates that they have established themselves as rather popular streamers with
concurrent viewership, i.e.heir viewers have the option to support them through a monthly
subscription. Both also frequently receive tips (donations) from their viewers. CutestSquirrel, the
streamer for the objectified condition has around 245,000 followers and CandyBar 104,000.
While this objectively sounds like a very large difference, on Twitch, the two channels still
remain in the same ballpark. Small streamers often have less than 1,000 followers. Mid-sized
streams that may apply for partnership usually have more around 5,000 and more followers. On
the other side of the extreme, a few superstar streamers amass up to over 1.5 million followers.
However, follower numbers for all popular streamers who can make a living off of Twitch but
are not superstar streamers, usually range from 80,000 to 500,000. When the channels were
selected, both streamers also had similar amounts of subscribers which can be estimated by the
number of unique emoticons they can offer on their channel. However, recently CandyBar has
surpassed CutestSquirrel in terms of monthly subscribers. She now has 20 emoticon slots and
CutestSquirrel only uses 17. Overall, there were no relevant differences in subscriber and
follower numbers. When observed, both channels had a rather active chat with around 300-800
concurrent viewers on average.
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Game choice focus: Variety gamer versus expert. While the selected streamers are not
exclusively playing one game or are professional competitive players known for a specific game,
they both mainly played League of Legends on their stream. Both, however, branched out into
other games from time to time but these streams were the exception rather than the rule.
Streaming League of Legends is what they are known for in the community
10.
League of
Legends is a competitive game in which teams of five play against each other. Within the game,
there are several different roles that players can self-select into. There is also a quite elaborate
ranking system for competitive play which is publicly accessible. This allowed for controlling
the actual gameplay skill level presented on the selected channels. For the nature of this study, it
was mainly important that the objectified streamer did not play on a significantly lower skill
level than the personalized streamer. If that were the case, it would be impossible to determine
whether participants rating her gameplay skill level lower were objectifying her or simply had
the game knowledge to determine that she was performing worse. CutestSquirrel’s main account
in League of Legends is currently ‘Diamond V’. In the game’s finely incremented ranking
system, this is a very high rank that only the top 1.5% of players manage to compete at. She also
plays on other accounts that are ranked lower
11
and closer to CandyBar’s rank. CandyBar was
ranked ‘Platinum IV’ at the beginning of the experiment and has recently been promoted into
‘Platinum III’. Her rank is still among the top 3.5% of all North American League of Legends
players. Given these percentiles, it is likely to assume that hardly any of the participants, or the
regular viewers watching the two streamers, play the game at the same level.
10
Both are mentioned on websites that compile popular female League of Legends streamers and rate them.
11
Many players choose to learn new champions or tactics on so called ‘smurf’ accounts to not risk a drop in
ranking on their main account.
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Focus on gameplay versus focus on streamer. Both women always stream with a
camera. During the selection process and after scanning her recent recorded videos it became
apparent that CutestSquirrel would spend more time talking before starting up the game, take
longer breaks talking to her stream and interacting with viewers and spend less time gaming
overall. CandyBar also took the time to start up the stream slow, talking to her viewers and
greeting her regulars. However, once she started gaming, she took less breaks in between and her
stream was also hardly ever listed under the ‘IRL’ category. CutestSquirrel, on the other hand,
often streamed under this category which did not require her to present a certain amount of
game-related content. Both women provided links to their social media profiles, such as Twitter,
Instagram and Facebook on their Twitch profile. They also both had a link for submitting
donations and some personal information about themselves. CutestSquirrel lives in Canada and
is half Romanian and half Vietnamese. CandyBar also lives in Canada and is Chinese. Both
women provide information on what languages they speak, what roles they play in their main
game and what rank they reached in past seasons. However, there are some notable differences
in the way they design their profile pages and how much emphasis they put on certain aspects.
While CandyBar briefly lists her subscriber perks and credits her top donors of each month (top
right titled “Hall of Fame”), CutestSquirrel prominently placed a “Wall of Fame” on her profile
page honoring contributors ranked by the amount they donated. Her highest donation of all
times: $9,140.32.
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Figure 4. CandyBar’s channel profile
Figure 5. CutestSquirrel’s channel profile
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CutestSquirrel also uses pink as a very dominant color for the headers on her profile and has
comic-like drawings of herself as headers. One of them shows her wearing a cat ear hair piece
blowing a kiss. CandyBar’s profile is organized a little differently due to her using a new Twitch
feature that is similar to a social media news feed on which streamers can post updates to their
followers. Followers will receive a notification with these updates. It is a feature Twitch has only
recently implemented to allow streamers to further enhance the communities around their
channels. Her panel headers are a black crown icon, her logo. CandyBar is ‘the Queen’. This is
also her account name in game. Overall, her profile looks a little more gender-neutral and
highlights her sponsor, the hardware manufacturer ASUS. It also features a link to her Discord
server that everyone can join. CutestSquirrel’s Discord server is only available to her monthly
subscribers.
Both streamers provide a lot of personal information and do not list computer
specifications or other game-related information. Obviously, their main focus is not merely on
the gameplay but rather on them as streaming personalities and their channel as a brand.
However, the Twitch avatar pictures they chose clearly demonstrate a major difference that has
also transpired through the design of their profile pages. CutestSquirrel’s picture shows her from
a slightly elevated angle looking up into the camera. Her top allows for quite a lot of visibility
for her cleavage. CandyBar’s avatar shows her wearing a black t-shirt with her channel name and
a cat picture. A merchandize article supporters were able to buy through Twitch. She looks
straight into the camera and the focus is clearly on her face and her channel’s name. With her
clothing and the camera perspective she mostly chose for the videos inspected during the channel
selection phase, CutestSquirrel puts a clear emphasis on her body and her sexual body parts.
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Figure 6. CutestSquirrel’s Twitch avatar (left) and CandyBar’s Twitch avatar (right)
These screenshots showing the two streamers during their most recent recorded streams
before the second survey was sent out to participants are a good example of what they typically
wore and how their streaming set up looked like. CandyBar’s webcam feed was usually a little
more than half of the size of CutestSquirrel’s camera. Not only is CutestSquirrel’s camera much
larger in relationship to the game screen, she also has a streaming overlay that makes it more
difficult to follow the game because the camera image and channel name sometimes block
important elements of the gameplay. It can be concluded that both streamers do not focus their
channel only on the gameplay. They both always stream with a camera, put on make-up and style
their hair. However, CandyBar, the streamer selected for the personalized condition, seemed to
not only interact with her viewers in a very light and friendly way, there were also many
interactions between viewers in chat. The regulars appeared to know each other and sometimes
game together, too. Whenever a subscriber alert popped up or someone donated money to her,
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CandyBar would thank them and say “when you’re happy, I’m happy!”. CutestSquirrel’s chat,
on the other hand, appeared to mostly be dominated by viewers addressing her with questions
rather than interacting with each other. Her subscriber alert is a gif of her dancing in her seat
which allows for a deep look into her moving cleavage. CandyBar’s regulars also celebrated the
addition of the new viewer into their community by greeting them with a certain subscriber-only
emoticon. These rituals indicated that she fostered an active community around her stream with
regulars interacting with each other.
Figure 7. CandyBar while streaming
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Figure 8. CutestSquirrel while streaming.
Control. In addition to randomly assigning viewers to one of the two experimental
conditions, a third group of participants was instructed to watch a tournament stream. Identifying
a channel serving as the control was the most difficult decision the project was confronted with
early on. Selecting potential streamers for the experimental conditions was rather straight
forward following the presented framework for categorizing channels. Initially, the most
reasonable solution seemed to select a male League of Legends streamer. However, a male
streamer would probably have effects that could bias the results. Not assigning viewers to a
certain streamers and just letting them watch any kind of channels would have probably had
uncontrollable effects from them watching a comparable amount of female streamers playing a
different game, for example. The selected tournament stream features the same game and was
pretested to be at least as entertaining as the selected female streamers. Due to it not being hosted
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by one personable or objectifiable private streamer, it was expected to not have any effects on
how female streamers and female gamers would be perceived. Participants were still confronted
with the same game and spent the same hours watching as well as responded to the same survey
items. It also provided a good control for items inquiring about gaming skill since participants
attentively responding to the survey items were expected to assess the gameplay skill levels
presented on the control stream as very high.
Summary of Chapter Three
This chapter first presented the criteria employed to categorize Twitch streams and select
potential channels to achieve the desired variation in the independent variable. The study used an
experimental design that aimed to examine differences between groups and within subjects in a
setting as close to a natural viewing experience as possible. After explaining how the framework
for categorizing streams was developed, the chapter followed the conventional method section
format and described participants, sampling strategies, considerations about power and precision,
measures, and research design. It concluded in a detailed description of the channels selected for
the experimental groups and the control group. The fourth chapter will present the results from
all three survey waves.
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Chapter Four: Overview of Results
This chapter will present a compilation of the results derived from the data collected
through survey waves and diaries. As so often when research endeavors embark onto exploring
relatively new phenomena rather than replicating existing studies, some results were rather
unexpected. The chapter will first present the results from testing the proposed hypotheses and
respond to the posed research questions in the conventional format. Furthermore, it will point out
unexpected but interesting findings that will add to the discussion presented in chapter five.
Descriptive Statistics Across Experimental Groups
As discussed in chapter three, the response rates plummeted moving from the screening
survey to the initial survey and from the initial survey to the second survey assessing effects after
the viewing phase. Clearly, a drop in response rate was to be expected given that the experiment
involved viewing an assigned channel for four weeks. Several potential participants emailed or
tweeted at the researcher requesting a reassignment or informing her that they have no interest in
watching this channel. While this was an overall setback that has negative effects on power and
precision of the overall study results it also provided an interesting initial result. Out of 55
participants assigned to the control and the personalized condition, 40 ended up completing all
survey waves. However, out of the 55 participants assigned to the objectified condition, only 29
ended up responding to all survey waves. Apparently, participants seemed to enjoy watching this
channel less or had other reasons for dropping out. Several of them will be highlighted
throughout the result section and discussed in the next chapter. As expected, some diary entries
participants submitted do not match up with the logged viewer lists from the channels they were
assigned to. In general, the female participants seemed to take the experiment more seriously and
watched more hours. However, the viewer list and chat logging can also be deceiving as people
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might have forgotten to log into their correct account, for example. One participant emailed the
researcher to inform her that she had been watching with her partner’s account because it was
logged in per default. Therefore, a certain amount of discrepancy between diaries and logs was
expected.
An important issue with experimental designs is that it needs to be safe to assume that
assignments to conditions are truly random and the groups do not present any differences that
could impact the results. The participant subsection in chapter three described participants in
each experimental group in greater detail. On average, participants in condition 1 were slightly
younger than the mean age in condition 2 and control. However, a simple one-way analysis of
variance (ANOVA) comparing the three groups across several variables revealed that these
differences in age were not statistically significant on the p < .05 level. The groups were also
compared on all other potentially influential variables, such as how many hours they watch
Twitch on average, how many streamers they follow, which criteria they deem important for
streaming success, why they watch Twitch, and how many of the streamers they follow are male.
The only difference significant on the p < .05 level was on an item that asked how much they
agree to be motivated by the demand to learn more about specific games. Participants assigned to
the control group had a slightly higher average demand for learning compared to the two
experimental groups. However, since most results are based on comparing the two experimental
conditions, this difference should not impact the results. On average, participants stated to watch
17.3 hours of Twitch in a normal week and responses ranged from 30 minutes to over 50 hours.
They joined Twitch 3.5 years ago and follow from 1 to 744 channels. The median for how many
channels participants follow was 56 and the mean 107.87, probably strongly influenced by the
heavy users. On average, they stated that 80% of the channels they follow are hosted by male
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streamers. Responses for how many channels they were subscribed to ranged from 0 to 10
channels with a mean of 1.74 and a mode of 1. When asked about the simplified motives for
watching Twitch, the most important reason for watching Twitch was to be entertained (M =
4.48, SD = .65), followed by the desire to learn about the game they are watching (M = 3.82, SD
= 1.1). Less important to them were interacting with the streamer (M = 2.95, SD = 1.42),
interacting with the community (M = 2.85, SD = 1.38) and getting to watch the best players (M =
3.04, SD = 1.27). Viewers assigned to the tournament condition, however, ended up watching the
stream for more hours than viewers assigned to both of the experimental conditions. One
plausible explanation for this discrepancy is that both female streamers had erratic schedules
during the viewing phase. Since this experiment was conducted in the field it was impossible to
plan for possible complications with streamers getting sick, traveling, streaming less or at
unexpected times. During the first two weeks of the four week viewing phase, the streamer
selected for the objectified condition streamed very late at night, much later than she used to
when the channels were selected and pretested. At the end of the first week, she decided to
switch the availability of her VODs to ‘subscriber access only’ which prevents everyone who is
not a monthly supporter from watching the recorded videos. The combination of her streaming
very late at night and the lack of availability of recorded videos caused many participants to view
less hours during the first ten days. The issue was resolved with the streamer. After the
researcher contacted her on Twitter, providing only minimal information about the experiment so
that the streamer would not behave differently, knowing she would be observed, she agreed to
switch the VODs back to being publicly accessible for the duration of the experiment. Given that
this switch was likely to result in a small financial loss to her, the researcher decided to
compensate her with $90 although the streamer did not ask for any compensation. Two weeks
94
into the viewing phase, the streamer selected for condition 2, the personalized condition, left the
country to visit her father during a life-threatening surgery. She did not stream for about ten days
and participants had to watch recorded videos instead of live streams during this time. However,
she returned before the four weeks ended and went back to her normal streaming schedule.
Possible other explanations and limitations resulting from these circumstances will be discussed
in the final chapter.
Perception of Twitch Streamers
Hypothesis H1a predicted that appearance will be perceived as more important for a
female Twitch streamer’s success than her gameplay and her personality. To test this hypothesis,
participants were asked to rate a set of criteria based on their importance for male and female
streamers’ success. For both, female and male streamers, personality was rated as most important
(female: M = 4.42, SD = .85; male: M = 4.50, SD = .80) for streaming success (refer to table 1).
Therefore, H1a was rejected. However, the results provided several other interesting findings.
Looking at bivariate correlations for the set of criteria presented to participants, association of
certain criteria and the direction of the relationship differ by gender. Personality and interactions
with the streamer and between viewers were weakly but significantly correlated. For female
streamers, the quality of gameplay was negatively correlated with the importance of their
attractiveness (face: r = - .264, p < .01; body: r = -.199, p < .05; sex appeal: r = -.622, p < .01)
12
.
The correlations for female streamers are compiled in table 2. These results implied that
participants either perceive a female streamer’s quality of gameplay to be important for her
success or her attractiveness. Refer to table 1 for a complete correlation matrix.
12
Since not all variables were normally distributed, nonparametric (Spearman’s Rho) were observed in
addition to Pearson’s correlations. These also resulted in the same significant relationships reported here.
95
Table 1
Bivariate Correlations Between General Success Criteria for Male Streamers
Note. These are the measures from the initial survey before the viewing phase. N = 105.
Significant differences indicated with * p < 0.05 and ** p < 0.01.
Personality
Quality of
Gameplay
Attractive-
ness of
Face
Attractive-
ness of
Body
Sex
Appeal
Interacti
on with
Streamer
Interaction
between
Viewers
Personality 1.000
Quality of
Gameplay
.058 1.000
Attractive-
ness of
Face
.133 .125 1.000
Attractive-
ness of
Body
.070 .096 .817** 1.000
Sex Appeal .045 .095 .770** .828** 1.000
Interaction
with
Streamer
.363** -.061 -.012 -.002 .079 1.000
Interaction
between
Viewers
.229* .066 .085 .131 .125 .415** 1.000
96
Table 2
Bivariate Correlations Between General Success Criteria for Female Streamers
Note. These are the measures from the initial survey before the viewing phase. N = 105.
Significant differences indicated with * p < 0.05 and ** p < 0.01.
Paired samples t-tests comparing the ratings or male and female streamers’ success
criteria within subjects resulted in significant differences for how important the quality of their
gameplay is for their success and how important it is for them to have an attractive face, body,
Personality
Quality of
Gameplay
Attractive-
ness of
Face
Attractive-
ness of
Body
Sex
Appeal
Interacti
on with
Streamer
Interaction
between
Viewers
Personality 1.000
Quality of
Gameplay
.169 1.000
Attractive-
ness of
Face
-.097 -.264** 1.000
Attractive-
ness of
Body
-.071 -.199* .880** 1.000
Sex Appeal -.010 -.262** .893** .890** 1.000
Interaction
with
Streamer
.307** -.004 .094 .042 .084 1.000
Interaction
between
Viewers
.359** .138 .002 -.015 .000 .379** 1.000
97
and sex appeal (refer to table 3). No differences were found for the importance of personality,
which is perceived as the most important success criterion for both, male and female streamers.
Running the paired samples t-tests separately for male and female participants, female
participants still rate sex appeal and attractiveness of a female streamer’s face as more important
for her success than for a male streamer. The difference in perceived important of quality of
gameplay, however, is not significant for the 23 female participants.
An interesting observation was also made when repeating bivariate correlations split by
participant gender. Male participants’ ratings of importance of gameplay for female streamers
were weakly but positively correlated with importance of their personality (r = .22, p = .048).
However, quality of gameplay is negatively associated with the importance of attractiveness for a
female streamer. In other words, male participants rated a female streamer’s personality to only
be associated with importance of quality gameplay. If a female streamer’s success is perceived to
be based on her attractiveness, her gameplay will be rated as less important. Personality is only
related to gameplay but not to a female streamer’s looks. Hypothesis H1b hypothesized the
opposite relationship for male streamers and predicted that personality and gameplay will be
rated to be more important success criteria for male streamers than their looks. Unsurprisingly,
this hypothesis was confirmed by the data.
98
Table 3
Paired Samples T-Test Comparing Success Criteria for Male and Female Streamers
Note. Criterion: 1 = personality, 2 = quality of gameplay, 3 = attractiveness of face, 4 =
attractiveness of body, 5 = sex appeal.
After examining the results for which criteria participants generally deem important for
male and female streamers, hypotheses H2a and H2b took a first look at differences between the
experimental conditions. H2a predicted significant differences in how participants rate their
assigned female streamer in terms of gaming skill. The objectifying condition was expected to be
rated less skilled at gaming than the personalized condition. The data collected after the viewing
period confirmed this hypothesis. Respondents were asked to rate the skill level of the gameplay
presented on their assigned channel on a scale of 1-10 (1 = very low skill, 10 = very high skill).
Participants assigned to the objectifying condition rated the skill level their streamer played on
Criterion M SD M SD t (23;80) p Lower Upper
Cohen's
d
1
4.460 .779 4.625 .824 -.941 .357 -.533 .200 -.196
2
3.333 1.129 3.792 .977 -1.848 .078 .055 -1.848 -.381
3
2.958 1.628 2.083 1.139 2.948 .007 .261 1.489 .632
4
2.417 1.558 1.792 1.179 2.005 .057 -.020 1.270 .422
5
2.630 1.663 1.708 1.160 2.832 .009 .247 1.586 .602
1
4.410 .882 4.460 .795 -.490 .625 -.253 .153 -.055
2
3.575 1.230 4.125 .919 -4.607 .001 -.787 -.313 -.526
3
2.713 1.407 1.840 .892
6.974
.001 .626 1.124 .846
4
2.490 1.387 1.650 .843
6.304
.001 .569 1.106 .762
5
2.363 1.334 1.550 .778
6.370
.001 .559 1.066 .805
Women
Men
95% CI Female Streamers Male Streamers
99
much lower (M = 4.89, SD 2.21) than participants assigned to the personalized condition (M =
6.67, SD = 1.72). An independent t-test comparing these means resulted in a large significant
difference between the two groups (t(59) = -3.512, p = 0.001). Given the sample size of 104, the
Cohen’s d for an independent samples t-test was quite large (Cohen’s d = 0.893).
H2b predicted that viewers will rate the personality and gaming skills of an objectifying
representation of a female streamer as less important for success compared to ratings of a
personalized portrayal. A simple one-way ANOVA comparing how important participants rated
the importance of the, by now familiar, criteria for success between conditions revealed that the
collected data confirms hypothesis H2b. Participants in the objectifying condition rated the
streamer’s attractiveness to be more important for her streaming success than her gameplay.
Respondents assigned to the personalized condition rated their streamer’s personality to be much
more important than her attractiveness. While the one-way ANOVA comparing all three groups
revealed strong differences for all the tested criteria, an independent t-test merely comparing the
two experimental conditions – without involving the control – resulted in the expected
differences for importance of personality, quality of gameplay, attractiveness of body, and sex
appeal but could not detect significant differences for interactions with the streamer and between
viewers as well as activities and, interestingly, attractiveness of face. These results support the
claim that viewers are more likely to sexually objectify the intended condition 1 because a
person’s face is considered to be a part of their wholesome identity from which sexual body parts
are perceived as disconnected when sexual objectification takes place.
While H2a and H2b examined specific effects of objectifying the female streamer in
condition 1 on how participants rated her in regard to streaming-related attributes, H2c predicted
more general effects of objectifying her. H2c expected participants in the objectifying condition
100
to rate their assigned streamer as less warm and competent than participants in the personalized
condition. Respondents were asked to state how accurately a set of ten adjectives representing
qualities associated with humanness describe their assigned streamer. On all ten adjectives, the
accuracy ratings varied heavily across assigned channels. The adjectives relating to agency and
competence resulted in high ratings of accuracy from participants in the control condition. Still,
participants assigned to the personalized condition (CandyBar) rated the competence-related
adjectives to describe their assigned streamer to be more accurate than participants assigned to
the objectifying condition 1. As expected, the personalized condition resulted in the highest
accuracy ratings for the adjectives describing her as warm, caring, likeable, welcoming, and
compassionate. Without effects resulting from objectification, the control condition was expected
to receive the lowest accuracy ratings for the adjectives related to warmth and compassion. The
control channel was not hosted by a personable streamer viewers can identify with over time but
rather by a set of different casters and hosts. However, the ratings did not differ much for control
and objectifying condition 1. Participants in the control condition even rated their ‘streamer’ as
more likeable than participants in the objectifying condition. Nevertheless, a one-way ANOVA
resulted in highly significant differences for all adjectives between conditions with the
personalized streamer generally perceived to be the most caring, compassionate and likeable and
still scoring higher on adjectives related to competence than the streamer in the objectifying
condition. Since it was likely that there were differences in the extent to which participants
objectify and negatively perceive the female streamers by gender, the analyses were again run
separately for each gender, too. Split by gender, the data resulted in significant differences for
female participants’ rating of streamers’ intelligence and competence. In addition to providing
important insights and a basis for possible effects resulting from sexual objectification,
101
hypotheses H2a through H2c also served as a manipulation check. Since all hypotheses were
confirmed it was safe to assume that the objectified streamer actually was more likely to be
objectified than the streamer in the personalized condition.
102
Table 4
Mean Comparisons for Skill, Success Criteria and Adjectives Between the Objectified and
Personalized Condition
Note. N (obj) = 30; N (pers) = 38. For some variables, equal variances coud not be assumed. The
presented t-values reflect the correction. However, Cohen's d values might be slightly yes
reliable. Hedge's g was calculated as an alternative to control for unequal sample sizes but the
values were so similar that the more common Cohen's d was reported.
Variable M SD M SD t(df) p Lower Upper
Cohen's
d
Gaming Skill 4.893 2.217 6.667 1.726 -3.512 (59) .001 -2.785 -.763 -.893
Personality 3.733 1.202 4.580 .599 -3.525 (40) .001 -1.330 -.361 -.892
Quality of
Gameplay
2.667 1.470 3.630 1.051 -3.035 (51) .004 -1.603 -.327 -.754
Attractiveness
of Face
3.167 1.642 2.763 1.218 1.124 (52) .266 -.317 1.124 .279
Attractiveness
of Body
3.130 1.814 2.320 1.297 2.083 (51) .042 .030 1.605 .514
Sex Appeal 3.033 1.810 2.237 1.218 2.069 (49) .044 .023 1.570 .516
Capable 3.870 1.224 4.868 .741 -3.947 (45) .001 -1.513 .491 -.986
Competitive 3.900 1.348 4.500 .952 -2.150 (66) .035 -1.157 -.043 -.514
Intelligent 3.800 1.095 4.658 .938 -3.477 (66) .001 -1.350 -.365 -.842
Competent 3.730 1.230 4.816 .834 -4.130 (49) .001 -1.609 -.556 -1.033
Independent 4.500 1.075 4.784 .672 -1.260 (47) .191 -.737 .169 -.317
Compassionate 3.800 1.243 4.816 .865 -3.968 (66) .001 -1.527 -.505 -.949
Caring 4.033 1.217 4.737 .921 -2.714 (66) .008 -1.221 -.186 -.652
Likable 3.770 1.675 4.760 1.025 -2.863 (46) .006 -1.697 -.296 -.713
Warm 3.833 1.510 4.763 .998 -2.907 (48) .006 -1.573 -.287 -.727
Welcoming 4.300 1.317 5.050 1.064 -2.607 (66) .011 -1.329 -.176 -.626
Personalized Objectified 95% CI
103
Effects from Viewing and Objectifying Female Streamers
The first two sets of hypotheses first examined whether participants generally perceive
different criteria to be important for male and female streamers’ success. Since results from
testing hypotheses H2a through H2c provided evidence for viewers assigned to the objectifying
condition actually perceiving their assigned streamer more negatively, the next sets of
hypotheses were tested to examine whether these effects might influence viewers’ general
perceptions of female gamers. Hypotheses H3a and H3b predicted that when presented with an
objectifying media portrayal, i.e. in this case the streamer selected for the objectifying condition
1, viewers’ negative perceptions will carry over and negatively influence their impressions of a
strong female role model, in this case a female professional League of Legends player.
According to the hypotheses, participants in the objectifying condition were expected to rate the
female esports competitor as less likely to succeed in her new team. H3b also predicted that
viewers in the objectified condition would rate the adjectives related to humanness to be less
accurately describing the invented esports competitor. However, these hypotheses could not be
confirmed. A one-way ANOVA comparing the groups resulted in no significant differences for
how respondents rated the likelihood of her succeeding in her new role. The only exception was
found for the adjective “likeable”. Participants assigned to the objectifying condition 1 rated the
adjective “likeable” to more accurately describe the presented esports competitor. Not only was
this result surprising because based on the presented theory, there should have been an effect.
The effect was also in the opposite direction than expected. While not significant on the p < 0.05
level, the direction of the non-significant differences for other adjectives and the predicted
likelihood of success were also opposite to the predicted effects. Repeating the analyses split by
gender revealed even more puzzling results. The male participants assigned to the objectifying
104
condition rated the esports competitor more likely to succeed than male participants assigned to
the personalized condition (F(1, 51) = 4.74, p = 0.034). In addition to rating her as more likeable,
the differences for male participants were also marginally significant for how accurately the
adjectives ‘intelligent’ (F(1,51) = 3.72, p = 0.059) and ‘caring’ (F(1,51) = 3.88, p = 0.054) were
rated to describe her. Despite the effects only being marginally significant, the direction of the
effect was still unexpected. Possible interpretations will be discussed in the last chapter.
The next set of hypotheses, H4a through H4c predicted positive effects of regularly
viewing female streamers on the percentage of a) overall gamers, b) esports competitors, and c)
League of Legends players viewers assume to be female. To test these hypotheses, the
differences were first assessed using a simple one-way ANOVA. For overall gamers as the
dependent variable, it did not result in significant differences between experimental conditions
and control group (for these hypotheses, both experimental groups were compiled into one group
since both assigned participants to watch a female streamer). Since which percentage of gamers
people assume to be female is a rather general belief about the world that is likely influence by
many factors, such as age, gender, which games someone plays, who they usually watch on
Twitch outside of the experiment etc. To further explore possible predictors for the dependent
variable, a linear regression model predicting onto the outcome variable ‘percentage of gamers
estimated to be female’ and controlling for age, gender, and assigned channel was tested. As
another possible predictor, the percentage of channels hosted by male streamers participants
stated to follow was added to the model. Significant on the p < 0.01 level, this model already
explained a moderate portion of variance within the outcome variable (adjusted R2 = .144) given
that this outcome variable was assumed to be a very general belief, influence by a multitude of
factors. Gender was found to be the best predictor for which percentage of overall gamers
105
viewers assumed to be female. Adding how participants responded to some of the sexism in
video games scale items, increased the explained variance by quite a bit (these predictors were
identified based on correlation matrices of the zero-order correlations observed between
variables). Generally believing that female gamers are bad players is associated to the belief that
a smaller percentage of overall gamers is female. The other predictors, agreement with the
statement that women playing video games bring down the quality of the game and believing
that video games are a man’s world in which women have no place were not significant as were
significant individual predictors in the model and added to the explained variance. Important to
note here is that these models obviously present data about correlations and do not make causal
claims. For all presented models, the general assumptions for linear regression were checked
(normal distribution of residuals, equal distribution of variances for residuals, absence of
collinearity).
106
Table 5
Linear Regression Model Predicting on the Percentage of Overall PC Players Participants
Estimated to Be Female
Percentage of overall PC players
estimated to be female.
Variable B SE Beta p
(Constant) 60.574 10.123 .000
Hours watched assigned channel overall .192 .131 .129 .144
Age .039 .243 .015 .873
Assigned channel (1 = female, 2 = control) -.818 2.683 -.027 .761
Gender -7.580 3.242 -.221 .021
% of followed streamers who are male -2.116 1.261 -.149 .097
"Most women are not good at gaming." -2.765 1.233 -.221 .027
"Video games are a man's world." 9.776 2.253 .552 .000
"Women bring down the quality of the
game."
-6.340 1.934 -.442 .001
Note. F(8,95) = 6.411 (p < .001); R2 = .351; adjusted R2 = .296
Similar to H4a, the one-way ANOVA for the outcome variable ‘percentage of esports
competitors assumed to be female’ did not result in significant differences between control
condition and female streamers. Again, linear regression models were employed to get a better
picture of which variables might be associated with differences between subjects on the outcome
variable. In addition to the variables also used as predictors in the model for H4a, the model
explaining the most variance in the outcome variable included whether participants watch
MOBAs (such as League of Legends) or FPS (such as Counter Strike). As before, the model
controlled for age, gender, percentage of followed male streamers, how many hours they had
watched their assigned channel overall and which channel participants were assigned to. It is
107
noteworthy that watching MOBAs was a significant individual predictor whereas watching first
person shooters and other games seemed to have no impact on the outcome variable.
Table 6
Linear Regression Model Predicting on the Percentage of Esports Competitors Participants
Estimated to Be Female
Percentage of esports competitors
estimated to be female.
Variable B SE Beta p
(Constant) 33.839 11.473 .004
Hours watched assigned channel overall .064 .142 .043 .656
Age -.273 .265 -.107 .305
Assigned channel (1 = female, 2 = control) -3.133 2.877 -.104 .279
Gender -.244 3.694 -.007 .948
% of followed streamers who are male -1.642 1.372 -.116 .234
"Most women are not good at gaming." -1.515 1.334 -.121 .259
"Video games are a man's world." 9.723 2.454 .549 .000
"Women bring down the quality of the
game."
-4.377 2.091 -.305 .039
Watching MOBAs (0 = no, 1 = yes) -6.700 2.868 -.232 .022
Watching FPS (0 = no, 1 = yes) -.237 2.812 -.008 .933
Note. F(10,93) = 3.473 (p = .001); R2 = .272; adjusted R2 = .194
108
While hypotheses H4a and H4b could not be confirmed, hypotheses H4c was partially
supported by the data. The one-way ANOVA resulted in a significant difference between control
and female streamers for the percentage of League of Legends participants assumed to be
female. Participants assigned to one of the female streamers estimated a larger percentage of
League of Legends players to be female. However, repeating the analyses for female and male
participants separately revealed that the female participants were influential for this difference.
Sixteen women who were assigned to watching one of the female streamers estimated a mean M
= 35.375 % (SD = 11.661) of League of Legends players to be female. Eight women assigned to
the control condition, i.e. the tournament stream usually not showing many female gamers,
estimated the percentage of female League of Legends players to be M = 14.125 % (SD =
11.679). The male participants assigned to the female streamers also estimated the percentage of
League players to be slightly higher but the difference was not significant. To control for several
factors, such as gender and percentage of followed male streamers another regression model was
fitted to the data. The model identified assigned channel and how many of the followed
streamers were reported to be male as significant individual predictors. Both are negatively
correlated to the outcome variable, i.e. having been assigned to watch the control rather than the
female streamer as well as following a larger percentage of male streamers was associated with
lower estimates of what percentage of League of Legends players was assumed to be female.
Possible explanations for the interaction between assigned channel and gender as well as for the
different findings for the three hypotheses predicting onto beliefs about which percentage of
gamers are female will be discussed in the next chapter.
109
Table 7
Independent T-Tests for Participants’ Estimates of Which Percentage of League of Legends
Players is Female, Split by Gender.
Note. N (female streamers) = 52 men & 16 women, N (control) = 29 men & 8 women
M SD M SD t(22; 79) p Lower Upper
Hedge's
g
Of all PC players 48.063 12.918 47.500 15.147 .095 .925 -11.711 12.835 .040
Of esports
competitors
24.500 19.211 10.625 13.627 1.818 .083 -1.955 29.705 .760
Of League of
Legends players
35.375 11.661 14.125 11.679 4.206 .001 10.773 31.727 1.759
Of all PC players 34.904 12.754 34.828 14.782 .024 .981 -6.155 6.308 .006
Of esports
competitors
14.827 13.934 12.724 11.154 1.661 .101 -.979 10.851 .160
Of League of
Legends players
32.21 12.779 27.276 12.900 .697 .488 -3.902 8.108 .381
Female Streamers Control 95% CI
Women
Men
Estimated
percentage of
female players
110
Figure 9. Participants’ estimates of which percentage of League players is female depending on
whether they watched a female streamer or the control.
Finally, hypotheses H5a and H5b predicted that regularly viewing an objectified portrayal
of a female streamer would result in more negative explicit beliefs about female gamers and
about women in general. As already mentioned during the literature review, these hypotheses
were very ambitious given the expected sample size and the duration of the experimental phase.
The effects would have had to be relatively large to be detected under these conditions. Both
hypotheses were not supported by the data. However, there were a few interesting and
noteworthy results that can help provide some insight into possible interpretations of the
collected data. No differences between experimental groups were found for the items on the
sexism in video games scale presented by Fox and Tang (2014) (H5a) and for scores on the
ambivalent sexism index (H5b) after viewing the assigned channels over the course of four
weeks.
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Female Streamers Control
Percentage of League of Legends players
estimated to be female
Women Men
111
Effects of viewing objectified portrayals on success criteria and sexist attitudes
towards streamers. In addition to testing for differences between experimental groups,
differences in which criteria were deemed to be important for streaming success were tested
within subjects, before and after the viewing phase. Paired samples t-tests with the dataset split
by gender resulted in a few significant differences for pretest-posttest measures between the
experimental groups. Viewers assigned to the objectifying condition rated a female streamer’s
sex appeal and the attractiveness of her face to be more important for streaming success after
having watched the selected channel for four weeks. For viewers assigned to the personalized
condition, the same test resulted in a decreased perception of importance of a female streamer’s
personality. Viewers assigned to both conditions perceived interactions between viewers and
streamer to be less important for a female streamer’s success after the experimental phase. No
significant differences were found for the control group or for the rating of a male streamer’s
success on Twitch.
Sexist attitudes towards gamers or women in general were found to not be impacted by
watching an objectifying portrayal of a female streamer but the perception of other streamers
might. However, perceptions of other streamers might be influenced. Some items from the
sexism in video games scale that seemed to also be applicable to female streamers were slightly
modified and presented to participants as part of the last survey. Again, no significant differences
were found across channels. Repeating the analysis for both genders separately resulted in a
significant difference for female participants on the item ‘female streamers are just trying to
sellout’ which is a common stereotypical perception of streamers that exploit their viewers for
their financial gains. Overall, the items modified for sexism against streamers were highly
112
correlated with the scores for hostile sexism which is likely the best predictor for sexist attitudes.
No interaction effects between age, gender, or other common demographics were found.
Table 8
Paired Samples T-Tests Comparing Participants’ Ratings of Success Criteria for Female
Streamers Before and After the Viewing Phase
Motivations to Watch Different Types of Twitch Channels
Strong significant differences were found for why participants assumed the different
channels would appeal to their regular viewers. Respondents were asked to rate their agreement
with how well a set of motivations described why other viewers might tune into the respective
channels. A one-way ANOVA resulted in significant differences across the channels for all the
Variable M SD M SD t(df) p Lower Upper
Cohen's
d
Personality 4.296 1.235 3.889 1.188 1.304 (26) .204 -.235 1.050 0.251
Quality of
gameplay
3.222 1.281 2.850 1.292 1.629 (26) .115 -.097 .838 .315
Attractiveness of
face
2.667 1.617 3.074 1.439 -2.096 (26) .046 -.807 -2.096 -.409
Attractiveness of
body
2.370 1.418 2.852 1.537 -1.999 (26) .056 -.977 .014 -.387
Sex appeal 2.222 1.396 2.890 1.625 -3.225 (26) .003 -.242 -3.225 -.634
Personality 4.530 .621 4.250 .762 2.183 (31) .037 .018 .544 .390
Quality of
gameplay
3.594 1.188 3.500 1.164 0.373 (31) .712 -.419 .607 .066
Attractiveness of
face
2.906 1.445 3.031 1.307 -.510 (31) .613 -.624 .374 -.091
Attractiveness of
body
2.719 1.397 2.750 1.295 -.122 (31) .904 -.553 .491 -.021
Sex appeal 2.625 1.385 2.750 1.459 -.528 (31) .601 -.607 .357 -0.094
Pre-viewing Post-viewing 95% CI
Objectified
Personalized
113
motivation items asked except for two items (motivation to unwind and to combat the feeling of
loneliness). Apparently, all channels were perceived to cater to social as well as tension release
motives to some degree. However, significant differences were found for how entertaining
channels were perceived to be. The tournament stream in the control condition was perceived to
be catering to viewers seeking to be entertained as well as to cognitive demands, i.e. learning
about new tactics. The personalized condition 2 was perceived to be scoring very high on social-
integrative motives. Viewers perceived being a part of the stream’s community to be much more
important than for the streamer in the objectifying condition 1. Across all motives, the channel
selected for condition 1 had the lowest scores. Her ratings were especially low on cognitive
motives, such as whether viewers seeking information on strategies or learn more about the game
would tune into her channel. On some of the items, homogeneity of variances could not be
assumed so the analyses were repeated employing a nonparametric test that is more robust to
heteroscedasticity. The differences found by the ANOVA were confirmed using independent
samples Kruskal-Wallis tests for analyzing motives across channels on almost all items. The
single exception was for the personal integrative item “They feel good when their comments
prove they have knowledge about the game.”. On a more robust nonparametric test correcting for
possible issues resulting from heterogenous variances, this item failed to result in differences
across channels significant on the p < .05 level.
114
Table 9
Overview of Differences Between Assumed Motivations for Participants Across Channels
Motive Variable Channel N M SD F (2, 103) p η²
1.000 30.000 3.100 1.242
2.000 38.000 3.974 .972
Control 38.000 3.000 1.230
Total 106.000 3.377 1.222
1.000 30.000 3.700 .794
2.000 38.000 3.947 .928
Control 38.000 3.763 1.076
Total 106.000 3.811 .947
1.000 30.000 3.467 1.074
2.000 38.000 3.921 .941
Control 38.000 2.868 1.189
Total 106.000 3.415 1.154
1.000 30.000 3.867 .937
2.000 38.000 4.000 .771
Control 38.000 3.526 1.310
Total 106.000 3.792 1.049
1.000 30.000 3.000 1.114
2.000 38.000 3.553 .921
Control 38.000 2.816 1.087
Total 106.000 3.132 1.079
1.000 30.000 3.900 .995
2.000 38.000 4.053 .733
Control 38.000 3.132 1.212
Total 106.000 3.679 1.074
.150 .000 9.084
.000 .151
2.086 .129 .039
5.120 .008 .090
Tension
release
Social
integrative
8.059
.644
9.192
.012
.001
.527
.135
They watch when there is
no one else to talk to.
Watching the stream makes
them less lonely.
The stream is relaxing.
The stream helps them to
unwind.
The stream is a pleasant
rest.
When watching the stream,
they don't have to be alone.
115
Motive Variable Channel N M SD F (2, 103) p η²
1.000 30.000 3.667 1.028
2.000 38.000 4.000 1.040
control 38.000 4.500 .797
Total 106.000 4.085 1.006
1.000 30.000 4.033 .765
2.000 38.000 4.211 .843
Control 38.000 4.579 .500
Total 106.000 4.292 .743
1.000 30.000 3.933 .785
2.000 38.000 4.368 .675
Control 38.000 4.605 .495
Total 106.000 4.330 .700
1.000 30.000 3.000 1.203
2.000 38.000 3.579 1.004
Control 38.000 4.421 .793
Total 106.000 3.717 1.144
1.000 30.000 2.067 1.311
2.000 38.000 3.342 1.258
Control 38.000 4.579 .793
Total 106.000 3.425 1.505
1.000 30.000 1.900 1.348
2.000 38.000 2.737 1.245
Control 38.000 2.000 1.139
Total 106.000 2.236 1.284
1.000 30.000 1.967 1.245
2.000 38.000 3.026 1.325
Control 38.000 4.474 .797
Total 106.000 3.245 1.517
1.000 30.000 2.133 1.408
2.000 38.000 3.237 1.324
Control 38.000 4.789 .413
Total 106.000 3.481 1.544
41.770 .000
4.899 .009
48.762 .000
41.539 .000
.087
.448
.486
.114
.093
.149
.254
.446
6.601 .002
5.281 .007
.000 9.004
17.572 .000
The stream is entertaining.
They enjoy watching the
stream.
They have fun watching the
stream.
The stream is exciting.
They can learn about new
game strategies.
The stream offers
information on new games.
The stream gives them idea
for special game tricks.
The stream helps them see
what game tactics are out
there.
Affective
Cognitive
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Motive Variable Channel N M SD F (2, 103) p η²
1.000 30.000 3.333 1.028
2.000 38.000 3.895 .831
Control 38.000 2.684 1.358
Total 106.000 3.302 1.205
1.000 30.000 3.267 1.413
2.000 38.000 3.947 .928
Control 38.000 3.395 1.242
Total 106.000 3.557 1.220
1.000 30.000 3.400 1.163
2.000 38.000 3.553 1.005
Control 38.000 2.211 1.277
Total 106.000 3.028 1.298
1.000 30.000 3.767 .971
2.000 38.000 3.868 .906
Control 38.000 2.184 1.353
Total 106.000 3.236 1.349
1.000 30.000 4.633 .615
2.000 38.000 4.289 .802
Control 38.000 2.447 1.483
Total 106.000 3.726 1.431
1.000 30.000 3.000 1.462
2.000 38.000 2.342 1.321
Control 38.000 3.605 1.220
Total 106.000 2.981 1.421
1.000 30.000 4.700 .651
2.000 38.000 4.132 .875
Control 38.000 2.184 1.136
Total 106.000 3.594 1.419
71.815 .000 .582
43.836 .000 .460
8.600 .000 .143
15.048 .000 .226
26.972 .000 .344
11.538 .000 .183
3.269 .042 .060
Personal
integrative
Additional
They watch to make fun of
the stream.
They like how the streamer
looks.
They like it when other
members take their
comments into account.
They feel good when their
comments prove they have
knowledge about the game.
They want their comments
to increase their reputation
amongst other user.
They like it when the
streamer takes their
suggestions into
consideration.
They enjoy it when the
streamer gives them
attention.
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Note. Channel 1 = objectified, 2 = personalized. The items for the motives affective, cognitive,
tension-release, social-integrative and personal-integrative were first employed by Sjöblom and
Hamari (2016). The additional items were added based on previous research by the author
(Uszkoreit, 2015).
Motive Variable Channel N M SD F (2, 103) p η²
1.000 30.000 3.400 1.037
2.000 38.000 3.868 .875
Control 38.000 2.921 1.217
Total 106.000 3.396 1.118
1.000 30.000 3.300 1.179
2.000 38.000 4.000 .697
Control 38.000 3.132 1.234
Total 106.000 3.491 1.115
1.000 30.000 3.200 1.126
2.000 38.000 3.711 .898
Control 38.000 3.421 1.244
Total 106.000 3.462 1.106
1.000 30.000 2.833 1.020
2.000 38.000 3.789 .963
Control 38.000 2.368 1.239
Total 106.000 3.009 1.238
1.000 30.000 2.800 1.215
2.000 38.000 3.526 .951
Control 38.000 3.500 1.289
Total 106.000 3.311 1.190
.019 .074
.001 .121
1.858 .161 .035
16.837 .000 .246
7.683 .001 .130
Social
integrative
7.120
4.094
They would like to be a part
of the community for a long
time.
Members of the channel's
community care about each
other.
Members of the channel's
community have shared
important events together.
It is important for them to
part of the channel
community.
They enjoy spending time
with other members of the
channel community.
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General findings on motivations. Overall, participants who were subscribed to more
channels also had more social-integrative motives and chatted more with streamer and
community. Chatting with the streamer was positively correlated with the number of active
subscriptions participants stated to currently have (r = .346, p < 0.01, N = 105) and so was
chatting with other viewers (r = .340, p < 0.01, N = 105). Watching more hours (heavy use) was
only correlated with following more channels but not directly related to chatting more or
preferring other suggested motives. Participants who stated that they like to see the best players
stream were more likely to not be interested in interactions with the community. They stated to
chat less when watching streams but watched streams together with friends more often (via
external voip software etc.). There was also a negative correlation between the motivation to be
entertained and how many channels participants follow. Apparently, entertainment is
experienced through engaging with fewer channel communities but likely on a more regular
basis as opposed to following a multitude of channels. Viewers who watch Twitch because they
want to learn more about the game do not seek entertainment through social interactions.
Summary of Chapter Four
Several significant differences were found across the experimental conditions and the
control. The most remarkable results were found for the difference in perceived skill and for the
percentage of League of Legends players, participants assumed to be female. Participants
assigned to the objectified condition rated their streamer as significantly less skillful at gaming
than viewers in the personalized condition. The women assigned to view one of the female
streamers estimated the percentage of female League of Legends players to be much higher than
the women assigned to the control condition. Overall, it can be concluded that the experimental
manipulation worked as intended and several effects from objectifying the female streamer
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selected for the objectifying condition were detected. In addition, a first-order cultivation effect
from viewing female streamers was found.
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Chapter Five: Discussion, Limitations and Directions for Future Research
This last chapter will first frame and interpret the results based on the previously reviewed
literature. It will situate it within the growing body of research on gaming and streaming and
highlight practical applications. The next section will provide a discussion of possible limitations
and how they impact the generalizability and interpretation of the results. The chapter will
conclude by pointing out directions for future research and summarizing the results.
Discussion and Interpretation of Results
General differences for female and male streamers. Hypotheses H1a and H1b
predicted that viewers familiar with Twitch will have different beliefs about which criteria are
generally important for a male or a female streamer’s success. Hypothesis H1a was rejected by
the data while the corresponding hypothesis about male streamers H1b was confirmed by the
data. The result is not necessarily surprising. For both, male and female streamers, personality
was the most important criterion for streaming success. Streamers fill very diverse niches on
Twitch. Some viewers might enjoy watching someone ‘rage’ at teammates or the game, others
might like friendly buddy-type personalities that they can have some (para-) social relationship
with. Therefore, there is no way to pinpoint down what exactly makes a streamer successful
since, to some degree, a streaming persona can be considered an act. For some streamers, their
streaming persona and brand is similar to their actual personality offline and for some it is a
performance. Nevertheless, a streamer needs to have a captivating personality that entertains
people. However, participants’ responses demonstrated that for a female streamer’s success it is
also very important to be attractive and have sex appeal. This was expected since women, in any
profession or context, are always judged more heavily based on their physical appearance than
men. Even during the Olympics, commentators talk about what female athletes wear or how they
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style their hair. The paired-samples t-tests revealed that within subjects, male streamers are
perceived very differently from female streamers and that viewers have different expectations of
them. While gameplay and personality were positively correlated for female streamers, the
quality of gameplay as a criterion for success was negatively correlated with the importance of
physical attractiveness. This result implies that viewers either appraise a female streamer based
on her personality and the gameplay content she provides or her looks. A more problematic
implication is related to findings from the Online Entertainers Group’s survey on Twitch
streamers (2015). One of the myths they tried to debunk through their research was that it is
easier for women to be successful on Twitch. This is a common misconception found within the
Twitch community. However, it resonates quite well with the presented results. For some
viewers, women on Twitch are either successful for their looks or their gameplay. They cannot
be a gamer and pretty. This also implies that there is an expectation for every successful female
streamer to also be attractive. A female gamer who provides high quality gameplay but does not
meet societal standards for attractiveness or decides to stream without a camera might therefore
only appeal to a specific audience. This significantly lowers the chances of success for many
female streamers. The statistics provided by the online entertainers group (2015) indicate that
overall, female streamers are not even close to being as popular as many male streamers and it is
more difficult for them to grow concurrent viewership. Perceiving attractiveness as more
important for streaming success than the quality of gameplay and focusing on a streamer’s looks
also increases the likelihood of sexually objectifying female streamers.
The impact of objectification on perceptions of different female streamers. The first
two hypotheses were tested on data collected before participants were assigned to view one of
the selected channels and asked for general opinions. Hypotheses 2a through 2c were tested
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using data collected after viewing an assigned channel over the course of four weeks. The results
from hypothesis H2b also served as a manipulation check since they indicated that viewers
perceived the two selected experimental conditions differently and in the intended way.
Hypothesis H2a predicted that participants will rate the gaming skill of the objectified streamer
lower than participants in the personalized condition. The control was to be expected to receive
the highest ratings given that it provided professional play on the highest level. The data
presented a large and statistically significant difference for skill level between the experimental
groups and the hypothesis was confirmed. The very high ratings for the control group indicated
that the items were interpreted correctly by participants and validly measured the perceived skill
level of gameplay provided on the channel. The difference between the objectified condition and
the personalized condition was even larger than expected (Cohen’s d = .89) and even held true
for female participants. The results indicate that men as well as women objectified the streamer
in the objectifying condition and consequentially rated the objectively better player to be less
skillful. As emphasized in chapter three, both streamers play mainly League of Legends on their
stream and the streamer selected for the objectifying condition has a much higher skill rank than
the personalized condition streamer. Since the channel selected for the objectified condition is
much more focused on the streamer – she spends more time talking and less time playing, her
camera image is much larger and she shows a lot of cleavage – viewers, who are already inclined
to perceive a female streamer’s attractiveness as more important than her gameplay, will pay
even less attention to her gameplay. Therefore, viewers are also more likely to assume that her
gameplay is of lower quality because they interpret her focus on her body as her feeling the need
to emphasize her appearance. The results resonate with other studies on sexual objectification
finding that sexually objectifying women lead to perceiving her as less capable and less
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competent (Heflick & Goldenberg, 2009). Hypothesis H2b predicted that participants assigned to
the objectified condition would rate her personality and the quality of her gameplay as less
important criteria for success compared to the personalized condition. Again, this hypothesis was
confirmed by the data. Both, male and female participants rated the objectified streamer’s
personality as less important and the attractiveness of her body as more important compared to
participants in the personalized condition. However, female participants did not rate the
objectified streamer’s gameplay as less important. The sample size of females assigned to the
conditions might be too small to detect differences. Only six women were assigned to watch the
objectified condition and eight women watched the personalized condition. Another plausible
explanation for this results could be that the consequences of objectification are slightly different
for female participants since they objectify women for other reasons than men (Vaes et al. 2011).
The decision to split attractiveness into attractiveness of the body and the face was made based
on reviewed literature. Research presented by Gervais et al. (2012) indicates that a person’s face
is often perceived as part of the personalized perception, i.e. part of the person as a whole.
Whereas the body – and especially the sexual body parts – can be perceived as disconnected
from the person as a whole. Therefore, how important the attractiveness of a streamer’s face was
rated is expected to be less affected by sexual objectification. The results from testing differences
between the perceived success criteria support this claim and confirm that the desired
experimental manipulation was achieved. Both streamers are objectively beautiful and pay
attention to their looks. They use make-up and style their hair. However, the streamer in the
objectified condition was selected because she puts more emphasis on her body. Therefore,
results for the differences for success criteria confirmed that participants perceived the
manipulation as intended and support the claim that sexual objectification is the mechanism
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causing the observed effects. While hypothesis H2a and H2b were predicting differences
between the two channels in regard to streaming and gaming, hypothesis H2c predicted them to
be perceived as less intelligent, capable and warm. This was operationalized using a set of ten
adjectives describing human qualities and participants’ rating of how accurately they describe the
streamer. The adjectives are more general and abstract. They might not be directly related to
actions observable through the live stream, such as skill at gaming. Significant and strong
differences were found between the experimental conditions for all adjectives except for
independent. It is possible that this adjective is either too abstract or that participants considered
both streamers to be dependent on their partnership with Twitch and their viewers, for example.
The differences were a little less obvious when split by gender. Female participants rated the
objectified streamer lower on the adjectives competent, capable and intelligent. For the other
adjectives describing attributes, such as warmth and compassion, no differences were found for
female participants. Male participants rated the objectified streamer lower on all adjectives
except for competitive and independent. Again, differences could have just been too small to be
detected given the small sample size for female participants. However, two alternative
explanations are plausible: 1) The objectified streamer simply presented herself as less
intelligent, and capable or 2) the effects resulting from objectifying another woman might vary
for women. Due to financial and time constrains extensively pretesting the selected channels by
assigning participants to watch several hours beforehand was not possible. Instead, two short
clips and images of potential streams were pretested on a small group similar to the participants
recruited for the actual experiment. After watching two 30-second video clips of both streamers,
thirty pretest participants rated the objectified streamer as less intelligent, capable, and
competent. As expected from a 30-second short clip, these clips did not include many pointers to
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why one of them should be objectively less intelligent and capable. Therefore, it is likely that
sexual objectification was the underlying mechanism causing these effects. While this is not
sufficient evidence to claim that the objectified streamer did not appear to be less intelligent
throughout the four weeks after all, it still provides some evidence that sexually objectifying the
streamer was responsible for the observed effects. However, the results are not completely
conclusive due to the lack of control over whether one of the streamers did appear to be less
intelligent, less competent, and less capable. Another plausible explanation for the observed
results is that the effects of (sexually) objectifying a woman could be slightly different for men
and women. The female participants perceived the objectified streamer as less intelligent but did
not see her as deprived of warmth and compassion. It is also important to keep in mind that
streaming is often a performance. It is entirely possible that the objectified streamer chooses to
present herself as a little ditzy and not very smart because it is what viewers expect of her and
benefits her brand. ‘Boob streamers’ are already blamed for exploiting their mostly male
viewers, taunting them with cleavage and tricking them into donating them money or gifts.
Appearing smart would likely result in a streamer fitting this stereotype to be judged as cunning
and deceptive. Obviously, these judgments would not work in her favor. However, at this point
this explanation is a mere speculation and calls for further research to investigate.
The impact of objectification on positive female role models. Hypotheses H3a and
H3b predicted that viewing and objectifying a female streamer will have a negative impact on
how a strong female role model, in this case a female esports competitor, is perceived. Both
hypotheses were not supported by the data. Based on research presented by Schooler (2015), the
esports competitor was expected to be perceived as less likely to succeed. Participants were also
expected to attribute human qualities to her to a lesser degree. Again, the ten adjectives
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describing human qualities were used to measure possible effects of the alongside presentation
after the viewing phase. The observed effects, however, were not as predicted. Male participants
in the objectified condition deemed the esports competitor more likely to succeed and rated her
as more likeable as compared to the personalized condition and the control. A possible
explanation for missing effects was that Schooler (2015) worked with an objectifying
advertisement as a stimulus. Participants in the present study were watching the streamer over
time and then only saw an image of her smiling into the camera during streaming. The images
did not show explicit sexual objectification, for example showing sexual body parts disconnected
from their face. The stimulus might not have been effective because it was not objectifying.
While this might be a possible explanation for an absence of the hypothesized effects another
explanation is needed for the observed results. For male participants, effects in the opposite
direction as predicted were found. These findings imply that the more plausible explanation of
these unpredicted effects is that participants in the objectified condition had a greater contrast
between the streamer they have been watching and objectifying and the alleged professional
gamer. As discussed in chapter two, women are often accused of not being real gamers. One of
the most common stereotypes about women in gaming is that they only play because they are
seeking the male players’ attention (Fox & Tang, 2014). After watching and objectifying the
streamer in the objectified condition, the male participants were therefore more likely to accept
this invented rising professional gamer as authentic and apparently, as more likeable. The
absence of this effect within the female participants assigned to the condition could, as already
elaborated on, be due to their small sample size (N= 6 women in the objectified condition). Since
hypothesis H2c did not detect significant differences in likeability between the personalized and
objectified group for female participants, it is also plausible that women simply had a different
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impression of the objectified streamer. The results found for hypotheses H2a through H2c
demonstrate that women in the objectified condition perceive the streamer in the desired manner
and do objectify her. However, they mostly rated her as less intelligent, capable, and competent
but not as significantly less likeable, warm, and caring. The absence of these effects can also
explain why the women in the objectified condition did not rate the esports competitor as more
likeable.
The impact of watching female streamers on beliefs about female players.
Hypotheses H4a and H4b were not supported by the data. However, the data collected after the
four-week viewing phase confirmed hypothesis H4c. H4a and H4b asked participants to provide
an estimate of which percentage of overall gamers (H4a) and esports competitors (H4b) they
assume to be female. It is likely that viewing a female streamer for a few hours over the course
of four weeks does not impact a belief as general as the estimated prevalence of female gamers.
Most players are aware of numbers published by the Entertainment Software Association or
other outlets estimating that around half of all gaming adults are women (ESA, 2016). This belief
is probably also dependent on a multitude of factors, such as gender, age, cultural upbringing,
which games people have played in the past, how often they talk to other gamers, whether they
know female gamers and many others. The failure to detect an effect might therefore be due to
the rather short exposure to a female streamer, which might not have been sufficient to result in a
measurable effect given the obtained sample size. The linear regression model predicting on the
hypothesized outcome variable showed that gender was the most important predictor for the
percentage of gamers participants estimated to be female. This is not surprising given that female
gamers will likely conclude that there are other women who also enjoy gaming. Many female
gamers are also advocates and rather active within the gaming community so it can be assumed
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that they are more knowledgeable about actual numbers. Just to clarify, the item on the survey
did ask specifically for PC-gaming to avoid confusion about mobile and social media games. In
regard to hypothesis H4b, merely watching the tournament stream that did not feature any female
players or viewing a female streamer did result in significant differences between the groups. A
possible explanation could be that the female streamers selected were not actively competing in
any tournaments and are therefore not related to esports despite playing a game that is a popular
esport. It could be that not seeing any women on the tournament stream might have resulted in
them underestimating the percentage of female esports competitors but since this probably only
reflects the common belief and few to no women play esports professionally, it had no
significant impact. H4c, however, hypothesized that viewing a female League of Legends player
stream would result in estimating the percentage of female League of Legends players to be
higher. This estimate is directly related to what participants were exposed to. The control
condition, on the other hand, watched League of Legends gameplay but never saw a single
female player in any of the tournaments. Therefore, the real-world belief can be directly
connected, or ‘mapped’, to the media content participants were exposed to. Williams’ (2006)
results for virtual cultivation effects resulting from playing a violent game demonstrated that
cultivation effects often only occur when the real world belief is relatable to the media content
(also Williams, 2010). Similar results were found in another study investigating cultivation
effects from video games (Chong, 2012). Furthermore, it is remarkable that the significant
difference between the participants assigned to one of the female streamers or the control
condition was mainly driven by the few female participants in the sample. For male participants,
the difference between the collapsed experimental conditions and control condition were only
slight and not significant on 95% confidence interval. The most applicable explanation for the
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very large observed effects for female participants is that they likely have no informed opinion
on how many women play League of Legends. Although 41.7% of women in the sample stated
to play games of the same genre, only 16.7% watch them on Twitch. 61.1% of the male
participants played MOBAs and 62.2% said to also watch them on Twitch. Almost all of the
male MOBA players in the sample also choose to watch MOBA streams on Twitch, whereas less
than half of the female MOBA players watch MOBA streams. Since most MOBAs do not feature
voice chat or other tools that identify other players’ gender, players do not usually know whether
they are playing with or against women. Given that sexual harassment might be the consequence,
it is unlikely that female players will randomly disclose their gender. Therefore, it is very
plausible that most women playing MOBAs see themselves in the minority. Probably even to a
higher degree than they actually are in the minority. Around 60% of the women in the sample
neither play games of this genre or watch them on Twitch. These women were probably not
aware of the possibility that a decent number of women could be playing this game on a regular
basis. Female participants who were then assigned to the control were exposed to a channel
never showing any female players. Therefore, possible prior beliefs were likely confirmed and
the women who had no prior beliefs about female MOBA players were confronted with their
apparent absence. Women assigned to one of the female streamers, however, were confronted
with a female streamer rather successfully and excessively playing League of Legends. As a
result, the female participants in the experimental conditions overestimated the percentage of
female League players while female participants in the control gave very low estimates. Despite
the small sample size, the difference in estimates between participants assigned to watch a
female streamer and the control was highly significant with a medium effect size (Hedge’s g =
1.759, r = .673). This finding is very interesting not only because it confirms theoretical
130
considerations about mappable scenarios for first-order cultivation effects. It also has a range of
practical implications. An example would be game publishers trying to promote games to female
gamers, a market that often remains untapped. The implications will be discussed in more detail
later in this chapter. An alternative explanation for this finding could be that participants
employed an availability heuristic, i.e. they did not have any other examples available to them on
which they could base their estimates.
The final set of hypotheses, H5a and H5b, predicted negative effects on attitudes towards female
gamers and women in general through watching and objectifying female streamers. Both
hypotheses had to be rejected. There were no differences across experimental groups and control
for items on the sexism in video games scale as well as on the ambivalent sexism index scores.
The findings are not necessarily conclusive given the very small sample size. Smaller effects ( r
< .3) cannot be reliably detected by a sample this small. It is likely that if effects occurred, they
must have been very small. While cultivation effects on negative attitudes towards women in
general are rather unlikely, a larger sample size and an extended time of exposure could possibly
still produce effects. Type II errors cannot be ruled out completely. The predicted power to
detect small effects (.1 < r < .3) for this sample size is only about .6. Results for comparing
pretest-posttest measures on general criteria for streaming success for male and female streamers
across experimental conditions and control revealed changes in the criteria mainly used to
distinguish the two experimental conditions. Viewers in the objectified condition rated the
importance of sex appeal as a general success criterion for female Twitch streamers higher after
watching CutestSquirrel for four weeks. This finding provides evidence for some sort of learning
or cultivation effect from watching certain female streamers. Based on the mapping principle
presented by Williams (2010) some sexist perceptions about female gamers are also more likely
131
to be cultivated than others through watching certain female streamers. For example, some items
on the sexism in video games scale directly relate to female gamers seeking favors and attention
from men (Fox & Tang, 2014). This is also a common sexist stereotype about female streamers
(OPG, 2015). Viewing female streamers and seeing this stereotypical perception come to life on
stream as a recurring pattern could help perpetuate such stereotypes. Possible directions for
further research examining mappable cultivation effects from streaming will be discussed later in
this chapter.
Categorizing different twitch channels and why viewers might watch them. Research
question RQ1 explored possible criteria for categorizing Twitch channels and also motivations
for viewers to tune into different types of channels. While the criteria for categorizing them
require a future research project to test their validity, varying motivations to watch the different
channels used in the present study were collected as part of the second survey after the viewing
phase. The second part of RQ1 mainly aimed to examine whether possible different motivations
to watch Twitch channels can be related to the proposed criteria for categorizing Twitch
channels. The observed differences for motivations across experimental channels and control
indicate that Twitch, similar to television or social media, is very versatile and certain types of
channels likely cater to certain types of demands. The personalized streamer scored highest on
social-integrative motives while the tournament stream trumped both experimental channels on
cognitive motives as well as need for entertainment and excitement. These results are reasonable
and expected after identifying and categorizing the selected channels. Based on findings
presented by Hamilton et al. (2014), being part of a community and interacting with other
regulars as well as the streamer is an important aspect of viewing Twitch. This resonates well
with what Sjöblom and Hamari (2016) found in regard to motives predicting subscription
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behaviors. Subscribing to a Twitch channel was best explained by social-integrative motives. A
channel that is not mostly built on interactions between the streamer and viewers or a focus on
gameplay but rather attracts people because of the streamer’s appearance could therefore have
lower subscriber numbers. The Twitch and Gender study revealed that while female streamers
often have an easier time growing a large following and get a lot of people to click on their
channel once, concurrent viewer numbers grow comparably slow (OPG, 2015). One of the
possible explanations for this phenomenon could be that viewers and subscribers seek to interact
with the streamer. That leaves them competing for her time and attention rather than bonding
together and forming a community. Objectifying the streamer and perceiving her as less warm
and welcoming will likely not lead to a warm and welcoming community forming around the
channel either (Hamilton et al., 2014). Therefore, building a tight-knit and welcoming
community on their channel could be more difficult for female streamers that put an emphasis on
themselves and their appearance rather than their gameplay or personality. The streamer in the
personalized condition has less followers but based on the number of unique channel emoticons
she has more subscribers whereas the objectified streamer generates more views and follows but
less subscribers. Both of these results fit the suggested narrative. The study on Twitch chat
comments conducted by Nakandala et al. (2016) found that all popular female streamers received
sexist and objectifying comments in chat. Common words on female streamers’ channels were
cute, beautiful, smile, babe, lovely, marry, boobs, gorgeous, omg and hot. Words commonly
found in male streamers’ chats were epoch, attempts, consistent, reset, shields, fastest, devs,
slower, melee and glitch. The results imply that although a female streamer might not necessarily
emphasize her physical appearance, a certain percentage of viewers will still pay more attention
to her looks than her personality or her gameplay. For male streamers focusing on gameplay and
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especially for expert streamers (as opposed to variety streamers), cognitive motives, such as
wanting to learn more about a game or discover the latest strategies will likely be the primary
motivations for viewers to join their channel. Similar motivations will likely attract viewers to
tournament streams. They want to see high level gameplay, learn the latest tricks and, of course,
be entertained by the competitive nature of esports. In return, they do not seem to care much
about interacting with the streamers and other viewers and tend to chat less. The results for
general motivations to watch Twitch, subscription behavior and chatting behavior also imply that
the key to subscribing is generally a lively channel community and being active in its
community. Feeling part of a channel’s community and socially interacting with other regulars is
then perceived as entertainment. The correlation between interacting with the streamer and other
viewers and the general motivation to watch Twitch as a source for entertainment implies a
connection between feeling entertained and being socially integrated. It is likely that the analyses
conducted by Sjöblom and Hamari (2016) failed to attain a higher percentage of explained
variance (the reported R2 for the regression model predicting onto subscription behaviors in their
study was only .037) in the outcome variable subscription because they did not distinguish to
which type of channels their participants subscribed to. Some streamers who have thousands of
viewers every time they go live on Twitch and a chat moving so quickly one can hardly see their
own message still amass thousands of channel subscribers. One of the possible incentives for
subscribing to very popular channels is gaining access to the unique channel emoticons.
Spamming a certain emote together with others can be interpreted as a form of social interaction.
However, the items inquiring about social-integrative motives seem to generally ask for a deeper
involvement into a community. On Twitch, not all communities and social interactions are
regular, long lasting, and deep. However, they are likely to still be social. Many other streamers
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limit access to information and tools they use. Subscribing is likely to not be based on social-
integrative motives here. Understanding why people watch Twitch and decide to chat, donate or
subscribe calls for further research that takes the versatile nature of Twitch content into account.
Limitations
One of the major limitations to the presented study is the relatively small sample size.
This limitation was already mentioned as part of the discussion of several results. For some
hypotheses, such as H5a and H5b, the small sample size and resulting restrictions in power to
detect effects could be responsible for the failure to reject the null hypothesis (type II or beta
errors). In addition, the smaller sample size limits the generalizability of the data. Despite
females being overrepresented within the sample as compared to the general population of
Twitch viewers, the very low number of women assigned to each condition constitutes a threat to
external validity. Fortunately, the study detected effects of very large sizes for many differences
which will still be generalizable to North American Twitch viewers of different game genres.
This generalization is possible since the study employed a very diverse sample regarding the
games participants watched and played. Funding for this study was limited to the doctoral
dissertation funding provided through the Annenberg School for Communication and
Journalism. Therefore, participants could not be offered adequate compensation for their time
which resulted in low response rates given the extensive time commitment. Response rates also
dropped quite significantly from the screening survey to the first survey and then again from the
first survey prior to the viewing phase to the second survey after. While the small sample size in
the objectified condition was problematic for conducting valid and reliable statistical analysis,
the dropout rate does not necessarily constitute a limitation in itself. It is assumed that
respondents self-selecting out of an assigned condition have a negative impact on the external
135
validity of these results. However, in case of the present study, the viewers dropping out were
likely to be more sexist and more biased against the ‘boob streamer’ stereotype than many of the
viewers completing all survey waves. Obviously, it is impossible to make this inference
conclusively. However, anecdotal evidence from tweets directed at the researcher and emails
from participants dropping out give reason to assume that the drop-offs are likely to have
contributed to an increased type II error probability rather than a type I error probability. A
pretest-posttest design could have helped mitigate this limitation since it would have detected
smaller changes within subjects. However, including measures of sexist opinions would have
likely resulted in more participants choosing to self-select themselves out of the sample. This
would have led to issues with external validity and perhaps even smaller differences due to
interesting subjects refusing to participate in any kind of study related to gender in gaming
issues.
Another limitation to the present study was a lack of control over possible alternative
explanations. While diaries of when participants watched were randomly connected to chatlogs
collected during the viewing phase, no other behavioral measures were employed to control for
participants not watching attentively or lying about how many hours they watched in their self-
reports. The decision to not use additional measures to control for attentive watching was made
based on how difficult recruiting turned out to be. Forcing participants to install a foreign piece
of software on their PC would have likely resulted in even lower response rates. Many
respondents also watched their assigned stream from their smart phone. Not offering this
possibility would have resulted in even lower response rates and possibly oversampling Twitch
viewers only using browsers and excluding mobile users. The lack of pretesting the stimulus
material and controlling for alternative explanations in regard to whether one of the streamers
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was really less likeable or seemed significantly less intelligent also limits the internal validity for
this measure to a certain degree. However, pretesting the 30-second short video clips resulted in
the same negative ratings of the objectified condition which supports the claim that
objectification was the underlying mechanism and not alternative explanations, such as her being
less intelligent. Other issues during the viewing phase that might have produced spurious effects
were the erratic streaming schedule of the streamer in the objectified condition. Her late-night
streams likely negatively impacted response rates. Participants assigned to the control condition,
watched the most hours on average; followed by the personalized condition. This might be due to
participants finding the tournament stream more enjoyable. Another possible explanation could
be the stable streaming schedule that made viewing more plannable; schedules for the LCS are
usually announced weeks in advance. The tournament stream also made it easier to jump in and
enjoy an exciting match since it is rather simple to follow the casting and understand when a
team is winning. Connecting to one of the female streamers, her personality, and the channel’s
community was likely to be more difficult and less rewarding for the brief period of time viewers
were assigned to watch their stream. Limiting her videos to only be accessible by her subscribers
could have also driven participants away from the streamer selected for the objectifying
condition. It was rather unfortunate timing but given the field experiment design the study
employed it was impossible to plan for such complications. Since the situation was dealt with
rather quickly its likely that possible resulting spurious effects were limited. Another possible
threat to external validity could be raised in form of the argument that CutestSquirrel, the
streamer selected for the objectified condition is purposefully selling her sex appeal to attract
more viewers. Certainly, this assessment is correct. She appears to be very much aware of the
fact that she is not only or even mainly providing gaming content and could therefore be seen as
137
complicit in prompting objectifying perceptions by her viewers. However, it is highly unlikely
that she is aware of the scale of these effects. Her gameplay seems to be very important to her, as
can be inferred from several of her tweets. In one recent tweet she posted a clip playing against a
former professional player from the NA LCS and winning a fight against him. The tweet implies
that the former pro-player insulted her as ‘boosted’ on her stream. Boosting someone means
helping a worse player climb into a rank they are too bad to be playing in by themselves. Many
popular female League of Legends streamers struggle with similar allegations. Pokimane,
probably the most successful female League of Legends streamer currently active posted a
YouTube video to her channel profile as proof that she has been promoted into her rank queueing
up for games by herself. Inferring from the presented data, it is very likely that many viewers
sexually objectify female streamers, some more and some less extensively. CutestSquirrel is very
representative for a prevalent type of streamer and the very low ratings of her gaming skill give
reason to believe that similar effects occur when watching similar streamers. Another plausible
explanation for women on Twitch constantly having to provide proof for their gaming skills is
the very common sexist stereotypes that all women are inherently bad at gaming (Taylor, 2012;
Fox & Tang, 2014). While streamers like CutestSquirrel are probably aware that accessorizing
their gameplay with sex appeal will affect their viewers’ perception of them, it is unlikely they
can assess the scope of these effects. It is also important to note that sexual objectification does
not imply that the objectified woman does not have any agency or cannot present herself in a
way that will prompt objectification. The effects that are relevant in the context of this work are
taking place on the receiver side. The receiver attributes less humanness, including less agency,
competence, and warmth to the objectified woman.
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Although hypothesis H4c found what can be interpreted as a first-order cultivation effect,
it is possible that there might be alternative processes at work. Cultivation theory would suggest
that heavy viewers demonstrate stronger effects. However, how many hours participants watched
their stream in total did not influence the size of the detected differences or the lack thereof. A
possible issue might be that the self-reports were overstating the number of hours participants
actually watched their assigned channels. From connecting behavioral data in form of chatlogs
and viewerlists back to the submitted viewing diaries, it can be inferred that the women in the
sample reported their hours more truthfully and stuck to the suggested hours. This would also
provide an explanation for why the first order cultivation effects found for hypothesis H4c was
mostly driven be female participants. A more detailed analysis of viewer lists will need to
reevaluate the self-reports. Nevertheless, it is unlikely that all participants overstated their
viewing and therefore, cultivation theorymight not provide the most adequate theoretical
framework. Future studies should investigate alternatives, such as social cognitive theory
(Bandura, 2001).
Directions for Future Research
One possible direction for future research endeavors, the results of the present study
pointed towards, would be investigating the interplay between stereotype activation and sexual
objectification. The study on objectification conducted by Heflick at al. (2010) reviewed in
chapter two found differences for appearance focus versus personality focus when observing an
image of the same person. These results imply that learned stereotypes and potential reactions to
them do not need to be present for objectification to take place. However, there clearly is an
important interaction between attained stereotypes and their implications for how we perceive a
person. These can influence whether we are likely to objectify them and go beyond the mere
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appraisal of appearance and sexual body parts. Further research into how certain stereotypes are
perpetuated and perceived – potentially employing known tropes from television and cinema
studies –is necessary to fully understand the interplay between these processes and the resulting
effects.
The modified items based on the sexism in video games scale that were used to measure
potential differences in sexist opinions about female streamers were all significantly and decently
strong correlated (correlation coefficients ranging from .2 to .6). As live streaming is constantly
growing in popularity, a sexism in streaming scale identifying certain sexist stereotypes of
female streamers could be a useful tool to reliably measure effects in future research projects.
These live streaming studies would not be limited to Twitch and video game live streaming.
Twitter, Facebook, YouTube and many other platforms now offer live streaming services. As
mobile Internet connections improve in regard to bandwidth, speed, and affordability, live
streaming will likely become more prevalent in our everyday media environment.
Since there were some significant differences in the pretest-posttest measures of general
streaming success criteria, it is likely that viewing (and objectifying) female streamers on a
regular basis will impact on the expectations viewers have of other female streamers. Anecdotal
evidence for this claim was already provided by streaming personalities, like Hafu, who talked
about her experiences with viewers on Twitch. Many viewers expect her to show cleavage and
put more emphasis on her body than her gameplay. In Hafu’s opinion, that is partially due to
other female streamers doing so (Roose, 2016). A longitudinal study examining changes in the
perceptions of different types of female streamers and the expectations viewers have of them
would provide interesting insights in how these expectations then shape the content created for
Twitch.
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The results clearly indicate that a more nuanced view of what motivates users to watch
different Twitch channels is necessary. First, there is a need for a content analysis of Twitch
channels employing a categorization system similar to the proposed criteria. This content
analysis could then be used as the basis for investigating motivations specific to certain channels.
Research projects, such as the longitudinal study of changing expectations in relation to the
created content as well as a system for categorizing channels and the motives to watch them
would also provide a solid basis for game developers, publishers, advertising partners, aspiring
Twitch streamers, and researchers to have a common foundation. This common foundation is
necessary for developing advertising or promotion strategies as well as supporting research
projects that build on each other to further broaden and deepen the knowledge of this rising new
media form. Again, live streaming is not limited to live streaming video games. There are
multiple applications and contexts for streaming and results should inform research across the
different contexts to which live streaming is already applied or will be applied to in the future.
Conclusions
The data collected as part of the present study confirms that male and female streamers
are generally perceived and judged differently. For both, male and female streamers, an
interesting personality is assumed to be the most important criterion for having success as a
Twitch streamer. Unsurprisingly, women are expected to be beautiful and put time into
optimizing their appearance. For male streamers, higher standards are set for their gameplay. The
quality of a male streamer’s gameplay is perceived to be quite important for his success. For
female streamers, on the other hand, it is either the quality of gameplay that is important for their
success or their appearance. Again, this was not necessarily a surprising result and is in part
representative for gaming culture as a whole. A woman can earn the status of a ‘real gamer’ by
141
proving that she is very good at the games she is playing. Often this will require her to
outperform male gamers since the common belief among many male players is still that women
are inherently worse at gaming (Taylor, 2012). One of the problems with this ‘legitimization’
process for becoming a ‘real gamer’ is that women who started gaming later than many men due
to societal pressures and a lack of socialization with this inherently male technology, often fail to
simply outperform male gamers. While a man does not need to proof that he can be a real gamer,
a woman has to constantly legitimize her status. As many other sexist stereotypes are still as
common as the belief that women are inherently bad at video games (e.g. playing for male
attention, only playing to customize avatars, always seeking favors from male players) female
players are expected to constantly prove that none of these apply to them. Give that as of now,
there are only very few women competing at the highest level of play, it is likely that many
women will not gain acceptance through their gameplay. If women fail to earn their status as
legitimate gamers for whatever reason, they are likely to be judged based on other criteria,
mainly their looks but also if they are amicable and nice (Holz Ivory et al., 2014). The way many
viewers perceive female Twitch streamers reflects these statements about the gaming world.
Studies, such as Nakandala et al.’s (2016) examination of Twitch chat comments provide
evidence that sexually objectifying popular female streamers is not the exception but rather the
rule. Most female streamers are not pro-level gamers. They accessorize their gameplay with an
attractive appearance which appeals to the Twitch audience, mainly consisting of young men.
The proposed system for categorizing streams distinguishes between channels who are hosted by
experts at a game and channels who put a focus on the streamers themselves. The present study
aimed to select two different kind of female streamers that can both be seen on the ‘person’ side
of this continuum. First, a streamer that corresponds to the prevalent ‘boob streamer’ stereotype,
142
which can be described as a female streamer often showing lots of cleavage and clearly
emphasizing her body and sexual body parts over her personality or the game. Many very
successful Twitch streamers can be categorized under this type. For example, Kittyplaysgames
(ranked 52 of all Twitch channels in popularity), the second most popular female streamer as of
May 2017 or LegendaryLea, one of the top five female streamers. Both of them have been
photographed in lingerie for Playboy magazine. It is important to note that there are not just a
few women who realized that there is a demand for sexy gamer girls with low cut tops.
Identifying a personalized streamer who was comparable to the selected potential candidates for
the objectified condition was much more difficult. There were only very few female League of
Legends streamers who did not wear low cut tank tops on a regular basis and spend lots of time
talking, dancing or honoring subscribers rather than playing the game. CutestSquirrel, the
streamer ultimately selected as the streamer for the objectifying condition, did not receive the
most indicative scores in the pretest. However, to precede the argument of having cherrypicked
an extreme example, the other potential candidates pretested were dismissed in favor of the less
obvious version. An email the researcher received after assigning participants to the
experimental groups, sent by a male participant assigned to CutestSquirrel’s channel, provides
further evidence for her fitting the known stereotype well (as opposed to being an extreme
example of a type).
The email read:
“Lena,
While I do appreciate the invite to the survey and did fill it out. getting the instructions to watch
that streamer is something I draw the line at. I am sure they might be entertaining but I refuse to
support "titty streamers" Looking at her past broadcasts there is 2 maybe 3 streams where her
143
cleavage is covered up and not exposed. Every other stream it is the focus of the stream. The cam
takes up almost 1/4 of the stream and its just sad and pathetic. I appreciate the invite but will
have to bow out because I won't be watching or supporting a channel like that in any manner.”
(dropped out participant assigned to the objectified condition)
Had this participant remained in the sample, it is likely he would not have had the most positive
opinion about the streamer. However, his email did sum up the factors that were used to select
CutestSquirrel as the streamer. It is without question that CutestSquirrel chooses to present
herself in this manner on stream. Sex sells and this is not news to her – especially to a viewership
consisting of 75% men, likely even higher for a MOBA game. However, it must have also
proven successful for her. Apparently, there are viewers interested in viewing the type of content
she creates. The main goal of this work was not to provide evidence for the existence of the
‘boob streamer’ stereotype. A content analysis employing the suggested criteria could provide
such evidence. The goal of this study was to identify whether viewers perceive different female
streamers as less capable, less warm, less intelligent and less skillful at gaming and whether this
perception has an influence on how they perceive female gamers, esports competitors, or women
in general. It is a common argument against ‘boob streamers’ that they are making the lives of
‘legit girl gamers’ more difficult. To a certain degree, the collected data presented evidence for
this claim. After viewing the streamer in the objectified condition, participants’ ratings of general
success criteria slightly changed. Sex appeal was considered to be more important for female
streamers’ success after watching CutestSquirrel stream. This implies that there is a learning
process which can, of course, lead to a self-reproducing system. The more viewers expect female
streamers to be very attractive and sexy, the more likely female streamers are to cater to this
demand, the more likely viewers are to see it as an important criterion and so on.
144
The reason why this cycle is problematic can be deducted from the results for hypotheses
H2a through H2c which showed that the female streamer in the objectified condition was
perceived as less warm, capable, competent, and welcoming. The main finding here was that her
gaming skill, despite objectively being the better player, was rated lower than the gaming skill of
the streamer in the personalized condition. Sexually objectifying the streamer catering to a
certain demand led to perceiving her as the worse gamer. A stereotype of female gamers very
common in gaming culture (Taylor, 2012; Fox & Tang, 2014). This demand she is catering to
seems to be quite prevalent on Twitch given that Nakandala et al. (2016) identified the word
boobs as one of the most commonly used words in female streamers’ chats. One might argue
now that these women are perfectly aware of their decisions about how to present themselves on
stream. However, that does not change the fact that most of them are providing gaming content.
Many of them even play on a very high skill level. However, using sex appeal to promote one’s
gameplay results in perceiving the quality of someone’s gameplay as worse. This further
increases the perceived divide between ‘real female gamers’ and ‘fake gamer girls’ mainly
looking for male players’ attention and, in the case of Twitch, their money.
The results for hypotheses H3a and H3b demonstrated that viewing and objectifying a
female streamer did not impact how a female esports competitor is perceived. This finding
provides evidence countering the claim that ‘boob streamers’ are making life harder for ‘real
gamers’. However, an extension of this work testing the effects on another streamer would be
necessary to conclusively determine whether there is no influence on how positive female role
models are perceived when presented alongside an objectifying portrayal. Hypotheses H5a and
H5b could not find any effects of watching and objectifying female streamers on sexist opinions
about female gamers or women in general. This was an interesting result given the finding for
145
the discrepancy on perceived gaming skill. Since the most common stereotype about female
gamers describes them as inherently bad at gaming, perceiving a female streamer’s gaming skill
as lower through objectifying her would have been a likely candidate for a mapping scenario.
However, cultivation theory suggests that heavy viewers of recurring patterns will be likely to
experience first- and second-order cultivation effects. The four-week long exposure time might
not have sufficed to cultivate opinions. As mentioned in the beginning of chapter two, Twitch is
a platform that rewards already successful content creators by displaying their streams more
prominently. Based on such a ‘the rich are getting richer’ principle, streamers can be expected to
cater to existing demands and not change strategies that have worked in the past. Therefore,
‘boob streamers’ might very well be a recurring pattern that could have cultivation or social
learning effects in the long run. For now, the results demonstrated that short term exposure to
one streamer and objectifying her caused viewers to perceive her as less human and less skilled
at gaming but this did not change their general attitudes towards female streamers, gamers, or
women in general.
The most groundbreaking finding derived from the collected data was that seeing a
female streamer play a game can have a great influence on whether viewers, and especially
women, belief that other women regularly play this game. It is a result providing evidence for the
claim that representation matters. It is also a finding that indicates that cultivation effects from
watching Twitch are entirely possible. Therefore, the relationship between games and the gaming
community and Twitch could be similar to the relationship between the real world and television.
Women often perceive themselves in the minority in gaming and both, male and female gamers,
believe most video game players are men (PEW, 2015). In most cases this is not necessarily a
misconception. However, women as well as men might sometimes overestimate just how rare
146
women playing certain games are. In part this misconception is likely to even be supported
through gender-masking strategies employed by women who want to just blend in and not be
perceived as an out-group. Being in the vocal minority within gaming communities has long
negatively impacted how women were treated in this male dominated space (Gray, 2012).
Changing this perception and normalizing female gamers could have a positive impact on how
likely it is for women to be placed in an out-group as opposed to the male in-group of gamers.
This process can give way to objectification and sexual harassment. For game developers and
publishers, this finding implies that Twitch (or video game live streaming in general) can be a
powerful vehicle for promoting games within market segments that often remain untapped
because marketers assume a lack of interest in the products and do not know how to reach the
portion of women possibly interested.
147
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Abstract (if available)
Abstract
Video game live streaming, the practice of broadcasting one’s gameplay via live streaming or video on demand platforms, has dramatically risen in popularity over the last five years. Whether broadcasters are professional esports competitors or casual gamers entertaining their viewers from their bedrooms, millions of users log into platforms, such as Twitch, to watch their favorite gamers play. Gaming culture, however, is infamous for lacking inclusivity regarding race and ethnicity, gender, age and sexuality. This dissertation used close observations and an experimental design to investigate 1) how different types of Twitch channels can be categorized and what could motivate viewers to watch them, 2) whether viewers objectify certain female streamers, and 3) how viewing and possibly objectifying female streamers influences viewers’ perception of female gamers and women in general. The study was conducted in the field, i.e. it assigned participants to watch one of two existing Twitch channels hosted by female streamers or a control (tournament stream). One of the female streamers was identified to build her channel and her brand around her good looks with a strong focus on her body. Therefore, she was also more likely to be objectified (objectified condition). The other female streamer’s channel focused more on her personality and gameplay (personalized condition). Participants were surveyed online before and after a four-week viewing phase. Analysis of variance, t-tests and regression models used to examine the data resulted in models showing that viewers in the objectified condition rated their assigned streamer’s gaming skill to be lower as well as rating her less intelligent, and less warm than viewers of the personalized condition. Viewers assigned to one of the female streamers (as opposed to the control) estimated the percentage of female gamers playing the game mainly played on all assigned channels to be higher. While pretest-posttest comparisons resulted in an increase in perceived importance of sex appeal for a female streamer’s success on Twitch for participants in the objectified condition, no such increase was found for participants in the personalized and the control condition. However, there was no evidence that viewing and objectifying female streamers can lead to more negative or sexist opinions about female gamers. Participants assigned to the objectified condition did not cultivate more sexist opinions than the subjects assigned to view the personalized streamer or the control. Cultivation theory appears to be an adequate theoretical approach to studying effects from viewing Twitch. Practical implications include possible strategies for promoting games to female streamers and underline the power this new media has in shaping users’ ideas about who plays games and what we expect of online entertainers.
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Asset Metadata
Creator
Uszkoreit, Lena
(author)
Core Title
Video game live streaming and the perception of female gamers
School
Annenberg School for Communication
Degree
Doctor of Philosophy
Degree Program
Communication
Publication Date
07/13/2018
Defense Date
06/07/2017
Publisher
University of Southern California
(original),
University of Southern California. Libraries
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Tag
gaming culture,gender in games,live streaming,OAI-PMH Harvest,online experiment,Twitch
Language
English
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Williams, Dmitri (
committee chair
), McLaughlin, Margaret (
committee member
), Wixon, Dennis (
committee member
)
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lena.uszkoreit@gmail.com,lena.uszkoreit@usc.edu
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Tags
gaming culture
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Twitch