Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces

TL;DR

This study applies the Interpersonal Theory of Suicide (IPTS) to large-scale online SI disclosures on Reddit, developing a theory-driven computational pipeline to label posts by IPTS Dimensions and RiskFactors and to identify lethally SI cases. It analyzes the language of supportive responses via LIWC and SAGE, revealing distinct patterns tied to each SI type, and evaluates AI chatbot responses (GPT-4o) under varying prompt contexts, comparing them to human responses and receiving expert input. The findings show online SI expressions align with IPTS constructs and that online responses differ by SI type, with AI delivering coherent but less diverse, highly formal, and sometimes less empathetic support that benefits from human oversight. The work advances theory-informed digital mental health interventions and highlights design, ethical, and practical considerations for integrating IPTS and AI in online crisis support, while acknowledging limitations such as lack of clinical validation and potential biases.

Abstract

Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Online spaces enable individuals to express SI and seek peer support. While prior research has revealed the potential of detecting SI using machine learning and natural language analysis, a key limitation is the lack of a theoretical framework to understand the underlying factors affecting high-risk suicidal intent. To bridge this gap, we adopted the Interpersonal Theory of Suicide (IPTS) as an analytic lens to analyze 59,607 posts from Reddit's r/SuicideWatch, categorizing them into SI dimensions (Loneliness, Lack of Reciprocal Love, Self Hate, and Liability) and risk factors (Thwarted Belongingness, Perceived Burdensomeness, and Acquired Capability of Suicide). We found that high-risk SI posts express planning and attempts, methods and tools, and weaknesses and pain. In addition, we also examined the language of supportive responses through psycholinguistic and content analyses to find that individuals respond differently to different stages of Suicidal Ideation (SI) posts. Finally, we explored the role of AI chatbots in providing effective supportive responses to suicidal ideation posts. We found that although AI improved structural coherence, expert evaluations highlight persistent shortcomings in providing dynamic, personalized, and deeply empathetic support. These findings underscore the need for careful reflection and deeper understanding in both the development and consideration of AI-driven interventions for effective mental health support.

Figures (2)

• Figure 1: A schematic representation on the causal pathway to lethal suicidal attempts based on the IPTSvan2010interpersonal
• Figure 2: Coherence scores by varying the number of topics ($k$) in BERTopics topic model.