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We used survival analysis to model user disengagement in three distinctquestions-and-answering communities in this work. We used the completehistorical data of {Politics, Data Science, Computer Science} Stack Exchangecommunities from their inception until May 2021, which include the informationabout all users who were members of one of these three communities.Furthermore, formulating the user disengagement prediction as a survivalanalysis task, we utilised two survival analysis techniques to model andpredict the probabilities of members of each community becoming disengaged. Ourmain finding is that the likelihood of users with even a few contributionsstaying active is noticeably higher than the users who were making nocontributions; this distinction may widen as time passes. Moreover, the resultsof our experiments indicate that users with more favourable views towards thecontent shared on the platform may stay engaged longer. Finally, the observedpattern holds for all three communities, regardless of their themes.

Survival analysis for user disengagement prediction: question-and-answering communities' case