  We used survival analysis to model user disengagement in three distinct
questions-and-answering communities in this work. We used the complete
historical data of {Politics, Data Science, Computer Science} Stack Exchange
communities from their inception until May 2021, which include the information
about all users who were members of one of these three communities.
Furthermore, formulating the user disengagement prediction as a survival
analysis task, we utilised two survival analysis techniques to model and
predict 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 the users 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 towards the
content shared on the platform may stay engaged longer. Finally, the observed
pattern holds for all three communities, regardless of their themes.
