Phrase-level pairwise topic modeling to uncover helpful peer responses to online suicidal crises

The study investigates how online peer responses on Reddit’s r/SuicideWatch support individuals experiencing acute suicidal crises, and which types of responses are perceived as most helpful. Grounded in suicide theories emphasizing temporal dynamics (e.g., Fluid Vulnerability Theory), the work addresses gaps in prior research that has largely focused on risk detection and triaging rather than the quality/helpfulness of peer support. The authors aim to: (1) characterize topics in users’ crisis posts and in peer comments; (2) examine associations between post and comment topics; and (3) assess how these associations relate to peer-perceived helpfulness (measured via upvotes). A secondary aim is to validate patterns using an independent sample of individuals with a history of suicidal crisis.

Prior work shows social media as a venue for peer support around stigmatized mental health topics; Reddit facilitates crowd-sourced interactions with minimal moderation. Research on r/SuicideWatch has focused on identifying suicide risk and triaging (e.g., automated detection) or on features eliciting responses, but less on evaluating helpfulness of responses. Theoretical frameworks such as the Fluid Vulnerability Theory, Interpersonal Psychological Theory, and Three-Step Theory contextualize acute vs. baseline suicide risk and the critical time window for interventions. Empirical literature indicates appropriate support can mitigate crisis states and potentially prevent progression to suicidal behavior, underscoring the importance of understanding what kinds of online peer responses are helpful.

Study 1 (Reddit data):

• Data source: Reddit r/SuicideWatch via Reddit API, spanning 2013-12-01 to 2015-05-31. IRB-approved (Protocol #19-06-5390).
• Corpus: 21,430 original user posts and 129,008 peer comments (mean 6.02 comments/post). Collected per-comment upvotes as a proxy for peer perceived helpfulness and computed response time (time to first comment).
• Phrase mining: Applied AutoPhrase to identify quality phrases from both posts and comments. Settings: minimum support = 2; phrase quality threshold = 0.75. AutoPhrase uses distant positive labeling (Wikipedia/Freebase) and robust negative sampling with a random forest classifier; features capture popularity, concordance, informativeness, and completeness.
• Topic modeling: Developed PairwiseLDA, a supervised pairwise topic model that jointly learns topics for original posts and for peer responses using both words and mined phrases, and learns associations between post-topic and response-topic distributions via a transformation matrix and a sigmoid link for pairing probability. Inference via collapsed Gibbs sampling.
• Topic number selection: Heuristic search with K^(P) in {3–10} for posts and K^(R) in {1–20} for responses, expecting K^(R) > K^(P). Evaluated models using topic coherence (UMass) and a volume-balance metric MRVG. Selected K^(P)=5 for posts and K^(R)=10 for responses due to higher coherence and balanced volumes.
• Baselines: Compared with traditional LDA (separate corpora) and an analogy of cross-lingual LDA; both yielded less interpretable topics than PairwiseLDA.
• Analyses: Assigned each document to its highest-probability topic; computed (a) topic volumes; (b) association matrix of post-topic vs. response-topic frequencies; and (c) average upvotes per topic pair.

Study 2 (Independent validation):

• Sample: Amazon Mechanical Turk community sample, IRB-approved (Protocol #18-12-5050). N = 491 participants; 42.16% (n=207) reported lifetime suicidal ideation (proxy for suicidal crisis history).
• Prompt: Open-ended responses to “How would your advice differ if they had thoughts of suicide?” (stop words/punctuation removed; included words with frequency > 5).
• Modeling: Structural Topic Model (STM) with suicidal crisis history as a covariate predicting topic prevalence. Tested 2–15 topics using hold-out likelihood, exclusivity, and semantic coherence; selected 12 topics. Coefficients interpreted as higher/lower prevalence among those with crisis history; identified eight significant topics (p<0.05) and presented representative excerpts.

Ethics and data availability: The Reddit data/code are available in Dataverse (DOI: 10.7910/DVN/BDZ4LI) and derived from a public dataset (Reddit comments corpus, archive.org).

Study 1 descriptive patterns:

• Activity: 21,430 posts; 129,008 comments; mean 6.02 comments/post. Monthly activity increased over time.
• Timeliness: Only 0.4% of posts received a first response within 1 minute; fewer than 8% within 5 minutes; about 64.9% within 60 minutes, indicating delays during an acute risk window.
• Upvotes: Long-tail power-law distribution (exponent k ≈ 2.101; R² = 0.9213). 18.0% of comments had ≥1 upvote; 6.1% had ≥2 upvotes; average upvotes/comment = 0.53. Comment length was not significantly correlated with upvotes (r = 0.036).

Topics in user posts (K=5):

• Psychological pain (41.2%)
• Relationship stress (25.5%)
• Psychiatric disorder (17.9%)
• Academic difficulty (11.2%)
• Financial stress (4.2%)

Topics in peer comments (K=10):

• Asking questions (31.3%; 16,798)
• Communication support (21.0%; ~11,239)
• Academic encouragement (11.3%; 6,062)
• Interest development (9.8%; 5,259)
• Life meaning (8.3%; 4,472)
• Distraction/entertainment (7.4%; 3,967)
• Professional help suggestion (4.4%; 2,355)
• Relationship/loss support (3.1%; 1,667)
• Thanks/appreciation (1.7%; 917)
• Treatment/medication (1.7%; 886)

Topic associations and perceived helpfulness (average upvotes by pair):

• Financial stress posts received the highest average upvotes across responses (0.80 vs. overall 0.53).
• Relationship stress posts had the second highest upvotes; within these, professional help suggestion responses had the highest average upvotes (0.79), while relationship/loss support received lower average upvotes (~0.41) despite being commonly used.
• Treatment/medication was the least upvoted response topic overall (avg 0.28 across several post topics).
• For psychological pain posts, life meaning responses achieved the highest average upvotes (~0.53) but comprised a modest share (~9.9%).
• For academic difficulty posts, academic encouragement was common and fairly upvoted (~0.52), but professional help suggestion, though infrequent (~4.2%), received the most upvotes (~1.17) in that context.
• Frequent response topics (asking questions, communication support, academic encouragement) comprised 63.6% of comments, whereas the most helpful topics (professional help suggestion, life meaning, relationship/loss support) comprised only 15.8% of comments.

Study 2 (STM validation):

• Identified 12 topics; eight significantly associated with suicidal crisis history (p<0.05). Individuals with crisis history were less likely to use topics emphasizing generic directives to seek professional help or reasons for living/faith (e.g., topics 1, 3, 8, 12 had negative coefficients) and more likely to suggest/encourage professional help (therapy), provide immediate/direct support, and contextualize crises (e.g., topics 5, 6, 9, 10 had positive coefficients). Representative excerpts corroborated nuanced preferences for how help is suggested (encouragement vs. directive).

The findings address the core research questions by demonstrating that (1) users’ crisis posts cluster into interpretable acute-context topics aligned with Fluid Vulnerability Theory, and (2) peer responses span a broader, more varied set of topics. Importantly, there is a misalignment between what peers most frequently provide and what is perceived as helpful by the community. Professional help suggestions, life meaning, and relationship/loss support garner higher perceived helpfulness relative to their frequency, whereas common behaviors such as asking questions and generic emotional support receive lower upvotes. Helpfulness is contingent on the pairing of post-topic and response-topic (e.g., professional help suggestions are especially well-received for relationship stress; life meaning for psychological pain). Study 2 suggests that individuals with lived experience may prefer supportive, encouraging suggestions of professional help rather than directive instructions or generic reasons-to-live content, highlighting nuances in tone and approach. Together, the results emphasize tailoring responses to the user’s expressed context and encouraging empathetic, non-coercive referrals to professional resources.

This work introduces PairwiseLDA, a phrase-level pairwise topic modeling framework that jointly learns topics for user crisis posts and peer responses and their associations, enabling analysis of how specific response types relate to perceived helpfulness. Applied to r/SuicideWatch, the model identified five user post topics and ten peer response topics, revealed mismatches between frequent and helpful response types, and showed that warm suggestions of professional help tend to be perceived as helpful. A validation with an independent community sample aligned with the emphasis on encouraging professional help and immediate supportive actions. The study suggests opportunities to improve informal online crisis support by educating peers to provide more of the response types associated with higher helpfulness and to respond more rapidly. Future research should capture original posters’ perspectives on helpfulness, assess outcomes (e.g., symptom reduction), generalize across platforms, and explore intervention designs that nudge responders toward empirically supported reply types.

Helpfulness was proxied by upvotes and by responses from an independent sample, not by the original posters’ direct ratings, limiting interpretability. Outcomes for crisis resolution were not measured. Data were from a single platform (Reddit), and user demographics were unavailable, affecting generalizability. Authenticity of some online comments cannot be guaranteed (potential trolling or inauthentic posts). Timeliness of responses was limited, with few replies within minutes, potentially missing critical intervention windows. Topic counts in tables may reflect rounding/formatting inconsistencies; however, percentages guide interpretation.