Gendered Conversation in a Social Game-Streaming Platform

Abstract: 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.

• The paper reveals that female streamers receive significantly more objectifying comments compared to male streamers in high-viewership channels.
• It employs advanced text analysis methods including neural embeddings and doc2vec, achieving 87% accuracy in gender classification.
• The study advocates for improved moderation practices to counteract gender bias and promote equitable interactions on digital platforms.

An Examination of Gendered Discourse on Twitch

The expansive growth of online social platforms has altered the dynamics of interpersonal communication and recreational habits. Among these platforms, Twitch - an interactive social game-streaming site - has garnered significant attention, engaging millions of users in video game streaming and discussions. The study titled "Gendered Conversation in a Social Game-Streaming Platform" rigorously examines the manifestations of gender disparities in this context, focusing on the pervasive discrepancies observed in chat interactions.

Key Findings and Methodology

The researchers utilized a large dataset encompassing over one billion chat messages across Twitch channels, employing computational text analysis to discern patterns of gendered dialogue. The study reveals substantive findings:

• Nature of Chat Messages:
  • Female Streamers: Receive a higher frequency of objectifying comments compared to their male counterparts. Notably, this type of dialogue proliferates in channels with higher viewership.
  • Male Streamers: Engage with audience interactions more centered around game-related content.
• Viewer Behavior: There exists a remarkable division in viewer participation, with numerous users posting comments exclusively in either male or female streams, highlighting entrenched gender-based preferences in channel engagement.

The research adopted advanced textual analysis techniques, including neural vector-space embeddings and predictive modeling, to not only differentiate gendered language but also classify the streamer's gender based on chat messages. Noteworthy is the doc2vec method's adeptness at capturing semantic nuances, which allowed for an impressive 87% accuracy in gender classification in channel content, thus underscoring the distinct conversational dichotomies present on Twitch.

Theoretical and Practical Implications

This paper underlines the complex interplay of social dynamics in digital spaces, contributing to the broader discourse on gender stereotypes and objectification. The confirmation of gendered behavior in a digital spectator platform like Twitch has significant implications:

• Theoretical Insight: It extends the understanding of how digital anonymity and broadcasting can perpetuate traditional gender roles and stereotypes within online gaming communities. Patterns of interaction observed in this study draw parallels with broader societal behaviors, providing a microcosmic view of gender inequality.
• Practical Applications: The findings advocate for reformed moderation practices on platforms like Twitch to foster gender-neutral engagement and mitigate objectification. The implemented methodologies could inform the development of automated approaches for identifying and addressing gender-biased dialogues across digital mediums.

Future Research Directions

An avenue for future exploration involves examining the ways streamers may either counteract or inadvertently contribute to the observed gendered dynamics, particularly focusing on how monetization strategies might influence interactions. Additionally, as emerging technologies for multimedia analysis advance, they offer potential for comprehensive analyses of streamer-audience interactions beyond textual data, encompassing audio and visual inputs.

Moreover, understanding and leveraging the influence of user-led moderation and community norms may help bridge the gap between heavily gendered, popular channels and smaller, potentially more balanced communities. Thus, future research could provide a roadmap for cultivating equitable social environments within gaming culture and beyond.