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1. An Exploration of Mental Health Discussions in Live Streaming Gaming Communities

   https://doi.org/10.3389/fpsyg.2021.575653  

   Gandhi, Reesha; Cook, Christine L.; LaMastra, Nina; Uttarapong, Jirassaya; Wohn, Donghee Yvette (March 2021, Frontiers in Psychology)

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   Live streaming is a unique form of media that creates a direct line of interaction between streamers and viewers. While previous research has explored the social motivations of those who stream and watch streams in the gaming community, there is a lack of research that investigates intimate self-disclosure in this context, such as discussing sensitive topics like mental health on platforms such as Twitch.tv. This study aims to explore discussions about mental health in gaming live streams to better understand how people perceive discussions of mental health in this new media context. The context of live streaming is particularly interesting as it facilitates social interactions that are masspersonal in nature: the streamer broadcasts to a larger, mostly unknown audience, but can also interact in a personal way with viewers. In this study, we interviewed Twitch viewers about the streamers they view, how and to what extent they discuss mental health on their channels in relation to gaming, how other viewers reacted to these discussions, and what they think about live streams, gaming-focused or otherwise, as a medium for mental health discussions. Through these interviews, our team was able to establish a baseline of user perception of mental health in gaming communities on Twitch that extends our understanding of how social media and live streaming can be used for mental health conversations. Our first research question unraveled that mental health discussions happen in a variety of ways on Twitch, including during gaming streams, Just Chatting talks, and through the stream chat. Our second research question showed that streamers handle mental health conversations on their channels in a variety of ways. These depend on how they have built their channel, which subsequently impacts how viewers perceive mental health. Lastly, we learned that viewers’ reactions to mental health discussions depend on their motivations for watching the stream such as learning about the game, being entertained, and more. We found that more discussions about mental health on Twitch led to some viewers being more cautious when talking about mental health to show understanding. 

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2. Twitch Users' Motivations and Practices During Community Mental Health Discussions

   https://doi.org/10.1145/3492824  

   Uttarapong, Jirassaya; LaMastra, Nina; Gandhi, Reesha; Lee, Yu-hao; Yuan, Chien Wen; Wohn, Donghee Yvette (January 2022, Proceedings of the ACM on Human-Computer Interaction)

   Live streaming is a form of media that allows streamers to directly interact with their audience. Previous research has explored mental health, Twitch.tv and live streaming platforms, and users' social motivations behind watching live streams separately. However, few have explored how these all intertwine in conversations involving intimate, self-disclosing topics, such as mental health. Live streams are unique in that they are largely masspersonal in nature; streamers broadcast themselves to mostly unknown viewers, but may choose to interact with them in a personal way. This study aims to understand users' motivations, preferences, and habits behind participating in mental health discussions on live streams. We interviewed 25 Twitch viewers about the streamers they watch, how they interact in mental health discussions, and how they believe streamers should discuss mental health on live streams. Our findings are contextualized in the dynamics in which these discussions occur. Overall, we found that the innate design of the Twitch platform promotes a user-hierarchy in the ecosystem of streamers and their communities, which may affect how mental health is discussed. 

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3. Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization

   https://doi.org/10.1145/3340531.3411925  

   Lai, Hsu-Chao; Tsai, Jui-Yi; Shuai, Hong-Han; Huang, Jiun-Long; Lee, Wang-Chien; Yang, De-Nian (October 2020, Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM ’20))

   In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision. 

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4. Volunteer Moderators in Twitch Micro Communities: How They Get Involved, the Roles They Play, and the Emotional Labor They Experience

   https://doi.org/10.1145/3290605.3300390  

   Wohn, Donghee Yvette (May 2019, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems)

   The ability to engage in real-time text conversations is an important feature on live streaming platforms. The moderation of this text content relies heavily on the work of unpaid volunteers. This study reports on interviews with 20 people who moderate for Twitch micro communities, defined as channels that are built around a single or group of streamers, rather than the broadcast of an event. The study identifies how people become moderators, their different styles of moderating, and the difficulties that come with the job. In addition to the hardships of dealing with negative content, moderators also have complex interpersonal relationships with the streamers and viewers, where the boundaries between emotional labor, physical labor, and fun are intertwined. 

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5. "I Want To See How Smart This AI Really Is": Player Mental Model Development of an Adversarial AI Player

   https://doi.org/10.1145/3549482  

   Villareale, Jennifer; Harteveld, Casper; Zhu, Jichen (October 2022, Proceedings of the ACM on Human-Computer Interaction)

   Understanding players' mental models are crucial for game designers who wish to successfully integrate player-AI interactions into their game. However, game designers face the difficult challenge of anticipating how players model these AI agents during gameplay and how they may change their mental models with experience. In this work, we conduct a qualitative study to examine how a pair of players develop mental models of an adversarial AI player during gameplay in the multiplayer drawing game iNNk. We conducted ten gameplay sessions in which two players (n = 20, 10 pairs) worked together to defeat an AI player. As a result of our analysis, we uncovered two dominant dimensions that describe players' mental model development (i.e., focus and style). The first dimension describes the focus of development which refers to what players pay attention to for the development of their mental model (i.e., top-down vs. bottom-up focus). The second dimension describes the differences in the style of development, which refers to how players integrate new information into their mental model (i.e., systematic vs. reactive style). In our preliminary framework, we further note how players process a change when a discrepancy occurs, which we observed occur through comparisons (i.e., compare to other systems, compare to gameplay, compare to self). We offer these results as a preliminary framework for player mental model development to help game designers anticipate how different players may model adversarial AI players during gameplay. 

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