The present research investigates how Twitch streaming contexts modulate participants’ use of abusive language, with emphasis on community gatekeeping. We look at three levels of context—platform, genre, and user—by examining changes in users’ language as they move across genres in the platform. In so doing, we ask (1) what does abusive gatekeeping look like on an online platform like Twitch that affords little opportunity for one-to-one communication, (2) how do users in various genres leverage abusive language to collaborate and compete as they shape their discourse communities, (3) are individuals’ abusive language behaviors affected by code-switching across genres, and (4) to what extent does language change involve a shift in the way abusive language is used, rather than an increase/decrease in abusive language itself? These questions are both broad and complex, in that general trends and patterns of Twitch users are interpreted in light of underlying motivations across context. As such, we utilize a mixed-methods approach, combining digital trace data (preliminary N = 27840964 chat logs from Twitch streams) with computational network analysis with qualitative methods such as ethnographic mapping and discourse elicitation, from surveys and interviews with Twitch users (expected survey N = 500; interview N = 55).
Assistant Professor, Graduate School of Human and Environmental Studies, Nara Institute of Science and Technology
Research Data Analyst, University of California Los Angeles
Research Assistant, Nara Institute of Science and Technology
Research Fellow, Nara Institute of Science and Technology