Bots
The technical component which ties together Twitch as a sociotechnical system for inclusive community building is the stream bot, which is a type of program “designed to engage in social spaces and perform automated tasks” (Seering et al., 2018). Bots both integrate with and run alongside Twitch, though are not inherent to Twitch themselves.
The architecture of bots consists of a separate back-facing user interface which streamers and moderators interact with, as well as a front-facing representative of the bot, usually appearing in chat or on stream, which performs set tasks and with which viewers may directly interact. Their algorithms vary bot-to-bot, but the general procedure entails taking in stream data (from the chat and other interactive Twitch functions), checking previously set parameters or issued commands from the streamer based on that data, and then producing a response when these conditions are met, usually in the form a message in chat or a visual representation on the stream itself.
Using bots, community-building on Twitch becomes more automated, but also easier to control and personalize. Their widespread use also encourages a sense of familiarity between viewers and bots, setting expectations for how to properly interact with each other and Twitch streamers.
The video diagram below is a visual walkthrough of how these processes work.
Twitch bots have come a long way from their Internet-Relay-Chat predecessors. While they have grown beyond the early, simple (and sometimes capricious) moderation functions of the past (Paolillo, 1999), they now in turn play an even more significant role in community management. By automating information sharing, stream layouts, “mini-games” and even advertising and promotional content (Seering et al., 2018), bots increasingly have become the go-to over humans in this regard.
That said, the rise of bots in community building could also be seen as concerning. As Seering et al. (2018) raise in their conclusions, are bot algorithms sufficiently effective in moderation, or is there perhaps reason to worry about too much oversight? We could take it a step further and ask, is it right to delegate to bots multiple roles for community building? Considering how little Twitch viewers actually interact with bots beyond commands (Seering et al. 2018), how effective are bots in this regard really? And if they are replacing traditional human moderators, are they making community-building less of a bespoke process and more of a thoughtless, homogenized one?
This is something we attempt to answer in our system analysis. While more research is needed to fully understand the impact of bots, we have concluded that bots still work, in various ways, in support of streamers and their communities, rather than dehumanizing and homogenizing them. As bots continue to evolve and acquire more functions on Twitch and in other streaming-based communities, it will be important to follow their development and their effects on community-building, for better or worse.
Feel free to check out our sources in our annotated bibliography.
