Online deviant behaviors in social media have become one of the major concerns of the public, with its prevalence and rapid and widely spread. Though massive research has been done to understand the antecedents and consequences of online deviant behaviors, academic scholarship is still far from a definitive explanation about how the dynamic of the behavior is embedded in its context. In this dissertation, for two forms of online deviant behaviors, incivility and trolling, three studies were conducted to a) examine the overall dynamic of incivility on the macro level, b) understand the mechanisms of trolls and victims on the meso level, and c) identify the underlying mathematical architecture of trolling behavior on the micro level. Study 1 provides a descriptive account of the incivility dynamic over the past eleven years by examining the trends of incivility in three major categories: political, non-political, and mixed. Using longitudinal data from Reddit that accounts for 95% of the entire Reddit universe across eleven years and relying on the combination of supervised machine learning models and traditional statistical inference, the study found that incivility consistently represents around 10% of total Reddit comments, with fluctuations that correspond to offline socio-political events and platform-specific policies. We also found that political groups tend to be more uncivil, and discussions in mixed groups that are not overtly political but nevertheless discuss politics are less uncivil than that in political groups. Study 2 investigated the aftermath of trolling on community dynamics by examining the likelihood and conditions in which individual users react toward trolls. Using a longitudinal behavioral dataset collected from popular video communities on YouTube, the study found that the valence of the trolling message, characteristics of the individual member, as well as the patterns of past engagement with trolls from other community members all played a role in predicting how an individual would react to trolls. In other words, well-connected users situated in densely connected communities with a prior pattern of engaging trolls are more likely to respond to trolls, especially when the trolling messages convey negative sentiments. Study 3 employed an information theoretical approach to deconstruct trolling behavior as a dynamic process to understand how people engage in such behavior. Using longitudinal data from Reddit’s active users and applied stochastic process and statistical inference, the study found that individuals engaging in trolling behaviors is a complex process that involves both internal influences and external influences from others they interact with. In addition, the hidden pattern of mathematical architecture shows a contagion effect of the behavior. Overall, the three studies demonstrated that online deviant behavior is nested in individual processing, community norms, and platform contexts.

This dissertation provides a comprehensive examination into social live-streaming services (SLSSs), using a mixed-methods approach to examine how streamer and stream level factors drive viewer use, and how use has down-stream consequences on individuals’ psychological well-being. SLSSs are a newer form of media content that combine video entertainment, user-generated content, and social media together in a live-setting. While well- known media platforms have added their own live-streaming capabilities, as seen through Facebook Live, Instagram Live, and YouTube Live, the most popular social live-streaming services, such as Twitch.tv, offer several thousands of channels with live professional and amateur content accessible to any and all viewers depending on their motivations, interests, needs, and mood (Spilker, Ask, & Hansen, 2020). Foundational SLSS research has examined broad-scale platform behaviors such as content and turnover in popular channels or interaction patterns (Kaytoue et al., 2012; Ford et al., 2017), needs and gratifications of users (Sjöblom et al., 2017; Hilvert-Bruce et al., 2018), and participation and gift-giving behaviors (Wohn et al., 2018; Bründl, 2018; Yu, Jung, Kim, & Jung, 2018). However, while SLSSs encompass a variety of categories such as “music,” “politics,” and “talk shows,” (Ask, Spilker, & Hansen, 2019), the majority of these studies have primarily examined video-game streaming rather than SLSSs as a whole (Harpstead et al., 2019). Additionally, due to the complex relationships between streamers and their viewers, past studies have specifically focused their research on the perspective of streamers, viewers, or the platform (Harpstead et al., 2019). The following dissertation aims to address these gaps by accounting for streamer characteristics, viewers’ attitudes and behaviors, and broader community trends across SLSS categories. Specifically, this dissertation examines antecedents and outcomes of viewers’ participation and financial commitment in order to gain a comprehensive understanding of how viewers’ behavior and relationship with streamers across SLSS categories contribute to the health of these communities as well as the psychological health of its members.

Contributions are key resources in online communities. In order to ensure long-term sustainability, online communities must continuously recruit, socialize, and retain members who contribute information, share support, and form social relationships with one another (Yang et al., 2017). In SLSSs, viewers can contribute through active participation, or content creation such as commenting, that facilitates further interactions (Bründl et al., 2017). Viewers can also support their communities through monthly financial contributions, demonstrating greater commitment and tangible support beyond active participation (Chen et al., 2013). While contributions provide quantifiable metrics of community growth, they may also be indicative of community health and engagement. Using APIs for data collection, the first study employs linear mixed models to analyze how audience size, moderator activity, gender, and content diversity relate to viewers’ active participation and financial commitment to a streamer in the leading SLSS, Twitch.tv. Larger audiences diminished individual participation and financial commitment while moderation sparked more contribution. Female streamers especially benefited from increased moderation, earning 2-3 times more in financial contributions compared to men, who streamed more frequently but attracted much smaller audiences. Streamers with greater content diversity garnered smaller audiences, but viewer contributions did not differ. Findings demonstrate overhead costs to viewer engagement and underscore individual users’ experience as indicative of community health.

Contributions not only affect communities as a whole, but also their individual members. How members use and interact in these online communities have consequences for individuals’ psychological well-being (PWB), a measure of optimal psychological health and functioning. Decades of research have examined how social media use such as active and passive participation affect individuals’ well-being (see Meier & Reinecke, 2020 for review). The second study employs a cross-sectional survey to examine how use of Twitch.tv, specifically, active participation, passive participation, and financial commitment, relate to users’ psychological well-being. Results from Structural Equation Models demonstrate that actively participating in a favorite streamers’ Chat is directly related to increased psychological well-being. Viewers’ social capital and parasocial relationship with their favorite streamer were examined as explanatory mechanisms. Structural social capital, or individuals’ social interaction ties, were instrumental in providing PWB benefits to viewers who actively participated and financially contributed to their favorite streamer’s channel. Findings underscore the value individual users’ active and committed experiences have on their access to social resources and well-being.Overall, this dissertation highlights the social nature of SLSSs, demonstrating how certain streamer-level factors can motivate users’ contributions and improve the quality of the streaming community, as well as how users’ contributions provide them access to social relationships and support that improve individuals’ psychological well-being. In providing a comprehensive mixed-methods analysis that captures the relationship between streamers, viewers, and the platform as a whole, future research can further explore antecedents and outcomes related to the unique social interactions that occur within SLSS communities.