Reddit user activity and language predict mental health help-seeking timing
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals
Computation and LanguageMachine LearningSocial and Information Networks
Summary
Many studies try to identify people who seek mental health help after they join special online groups, but this paper looks earlier to see if help-seeking can be predicted before that happens. The authors studied Reddit posts and behaviors to find clues about which users might seek help and when. They found that how users behave online (like posting patterns) often predicts if they will seek help better than their words, but the language they use helps predict how soon they will seek it. Separating these two questions gives a clearer picture of help-seeking signals.
What this means in practice
- •For mental health platform teams: Design algorithms to identify users likely to seek mental health support using behavioral and language signals before they enter clinical communities.
- •For social media moderation teams: Improve detection systems for prioritizing outreach by predicting which users may soon seek mental health help based on their online activity patterns and text.
Authors
Nisar Ahmed, Ammara Kazmi, Irtaza Bashir Raja, Didier Devaurs
Abstract
Most prior work on predicting mental health help-seeking treats it as post-hoc classification of users already active in clinical communities, which cannot tell whether someone approaching a transition can be identified in advance. We instead frame help-seeking as a time-to-event problem and ask which Reddit users will eventually seek mental health support, and how imminent that transition is. We build a two-arm cohort: a control frame of 6,337 ordinary users with genuine right-censoring (12.1% transition rate) to answer who will transition, and a clinical frame of 5,023 eventual transitioners to answer how soon. Features come only from posts written before any clinical activity, and user-grouped five-fold cross-validation plus a four-year temporal holdout guard against leakage. We compare TF-IDF, MentalBERT embeddings and behavioural activity features using Cox proportional hazards models, Random Survival Forests and an LSTM sequential model. Behavioural features alone outperform text for predicting who will seek help (C-index 0.797 vs. 0.763), and combining both is best (0.823 under cross-validation, 0.856 on the holdout). The pattern reverses for predicting how soon a known transition will happen: text remains a stable predictor (C-index 0.698) while behavioural features become unstable. Structural engagement patterns therefore carry information about future help-seeking beyond linguistic content, and separating who from when yields a cleaner analysis.