Suicidal Ideation in Online Spaces Through the Lens of Interpersonal Theory of Suicide: Exploratory Study of Self-Disclosure, Peer Support, and AI Responses.

Background: Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Global estimates suggest that the lifetime prevalence of SI ranges between 9% and 12% worldwide, underscoring the scale of this public health concern. Online platforms, such as Reddit, provide spaces where individuals express suicidal thoughts and seek peer support. While prior computational research has leveraged machine learning and natural language analysis to detect SI, much of it lacks grounding in psychological theory, limiting interpretability and intervention design. Objective: This study applied the Interpersonal Theory of Suicide (IPTS) to understand the underlying psychosocial mechanisms driving high-risk suicidal intent in online spaces, analyze linguistic expressions of SI, and assess the role of artificial intelligence (AI) systems in providing supportive responses. Methods: We analyzed 59,607 posts from Reddit's r/SuicideWatch community. Posts were categorized into 4 SI dimensions (ie, loneliness, lack of reciprocal love, self-hate, and liability) and 3 IPTS-based risk factors (ie, thwarted belongingness, perceived burdensomeness, and acquired capability for suicide). High-risk posts were operationalized based on the language markers of suicidal planning, attempts, and explicit intent. We further conducted psycholinguistic and content analyses of supportive responses and evaluated AI chatbot-generated replies for structural coherence and empathy. Results: High-risk SI posts contained frequent references to planning and attempts, methods and tools, and expressions of weakness and pain, patterns that are consistent with theoretical expectations regarding the progression of suicidal capability. Supportive peer responses varied significantly across SI stages (P<.001), with deeper empathy and self-disclosure emerging in replies to high-risk posts. Compared with online community responses, AI-generated replies showed higher semantic similarity (Cohen d=0.20) and linguistic style accommodation (Cohen d=0.08), but substantially lower diversity (Cohen d=-0.31); empathy differences were minimal in the most context-rich prompting condition. Expert evaluators further noted that AI responses often lacked contextual personalization and emotional depth. Conclusions: Grounding computational analysis in IPTS provides richer theoretical insight into SI expressed online. While AI-based systems can enhance the structural and linguistic quality of supportive messages, they currently lack the nuanced empathy and contextual awareness needed for effective mental health support. These findings highlight the need for theory-driven, human-AI collaborative frameworks in suicide prevention research and interventions.