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

Abstract

We used survival analysis to model user disengagement in three distinct questions-and-answering communities in this work. We used the complete historical data from domains including Politics, Data Science, and Computer Science from Stack Exchange communities from their inception until May 2021, including information about all users who were members of one of these three communities. Furthermore, in formulating the user disengagement prediction as a survival analysis task, we employed two survival analysis techniques (Kaplan–Meier and random survival forests) to model and predicted the probabilities of members of each community becoming disengaged. Our main finding is that the likelihood of users with even a few contributions staying active is noticeably higher than those who were making no contributions; this distinction may widen as time passes. Moreover, the results of our experiments indicate that users with more favourable views toward the content shared on the platform may stay engaged longer. Finally, regardless of their themes, the observed pattern holds for all three communities.