  Online social media and games are increasingly replacing offline social
activities. Social media is now an indispensable mode of communication; online
gaming is not only a genuine social activity but also a popular spectator
sport. With support for anonymity and larger audiences, online interaction
shrinks social and geographical barriers. Despite such benefits, social
disparities such as gender inequality persist in online social media. In
particular, online gaming communities have been criticized for persistent
gender disparities and objectification. As gaming evolves into a social
platform, persistence of gender disparity is a pressing question. Yet, there
are few large-scale, systematic studies of gender inequality and
objectification in social gaming platforms. Here we analyze more than one
billion chat messages from Twitch, a social game-streaming platform, to study
how the gender of streamers is associated with the nature of conversation.
Using a combination of computational text analysis methods, we show that
gendered conversation and objectification is prevalent in chats. Female
streamers receive significantly more objectifying comments while male streamers
receive more game-related comments. This difference is more pronounced for
popular streamers. There also exists a large number of users who post only on
female or male streams. Employing a neural vector-space embedding (paragraph
vector) method, we analyze gendered chat messages and create prediction models
that (i) identify the gender of streamers based on messages posted in the
channel and (ii) identify the gender a viewer prefers to watch based on their
chat messages. Our findings suggest that disparities in social game-streaming
platforms is a nuanced phenomenon that involves the gender of streamers as well
as those who produce gendered and game-related conversation.
