In the course of every member's integration into an online community, a decision must be made to participate for the first time. The challenges of effective recruitment, management, and retention of new users have been extensively explored in social computing research. However, little work has looked at in-the-moment factors that lead users to decide to participate instead of "lurk", conditions which can be shaped to draw new users in at crucial moments. In this work we analyze 183 million messages scraped from chatrooms on the livestreaming platform Twitch in order to understand differences between first-time participants' and regulars' behaviors and to identify conditions that encourage first-time participation. We find that presence of diverse types of users increases likelihood of new participation, with effects depending on the size of the community. We also find that information-seeking behaviors in first-time participation are negatively associated with retention in the short and medium term.
https://doi.org/10.1145/3313831.3376151
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)