Time-to-event modeling of subreddits transitions to r/SuicideWatch
Xueying Liu, Shiaofen Fang, George Mohler, Joan Carlson, Yunyu Xiao

TL;DR
This study employs time-to-event modeling to analyze the temporal dynamics of Reddit users transitioning to r/SuicideWatch, identifying key subreddit features that predict earlier posts on r/SuicideWatch.
Contribution
It introduces a Cox proportional hazards model incorporating text and network features to predict the timing of users' transitions to r/SuicideWatch.
Findings
r/depression linked to earlier transitions
High-risk posts on r/Wishlist precede r/SuicideWatch by 10.2 days
Several features significantly predict transition timing
Abstract
Recent data mining research has focused on the analysis of social media text, content and networks to identify suicide ideation online. However, there has been limited research on the temporal dynamics of users and suicide ideation. In this work, we use time-to-event modeling to identify which subreddits have a higher association with users transitioning to posting on r/suicidewatch. For this purpose we use a Cox proportional hazards model that takes as input text and subreddit network features and outputs a probability distribution for the time until a Reddit user posts on r/suicidewatch. In our analysis we find a number of statistically significant features that predict earlier transitions to r/suicidewatch. While some patterns match existing intuition, for example r/depression is positively associated with posting sooner on r/suicidewatch, others were more surprising (for example,…
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Taxonomy
TopicsComplex Network Analysis Techniques · Mental Health via Writing · Mental Health Research Topics
