Time-to-event modeling of subreddits transitions to r/SuicideWatch
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Abstract (arXiv)
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, the average time between a high risk post on r/Wishlist and a post on r/suicidewatch is 10.2 days). We then discuss these results as well as directions for future research.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 31 references that are in the map. First posted on arXiv 13 Feb 2023.
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5 of 31 references.
References · 5
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 173
- 2019Suicidal Ideation Detection: A Review of Machine Learning Methods and Applicationscited by 31
- 2021BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patientscited by 15
- 2020Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classificationcited by 2
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