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

2025-04-17

AI Summary

This study investigates the risk determinants of suicidal ideation (SI) in online spaces and evaluates the efficacy of AI-supported responses, grounded in the Interpersonal Theory of Suicide (IPTS). Method: For the first time, IPTS is systematically operationalized for online SI modeling. A multimodal analysis was conducted on nearly 60,000 SI-related Reddit posts, integrating psycholinguistic coding, NLP feature extraction, machine learning classification, and expert human annotation to construct an IPTS-driven multidimensional labeling framework. Results: High-risk SI strongly correlates with explicit suicide planning, method description, and subjective distress expression; supportive responses vary dynamically across SI stages; while AI chatbots improve response coherence, they exhibit fundamental limitations in empathy depth, contextual understanding, and personalized intervention. Contribution: The work establishes a validated IPTS-based paradigm for online SI research, uncovers linguistic mechanisms underlying SI risk, and delineates theoretical boundaries and practical implications for AI-enabled mental health interventions.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Natural Language Processing: Safety and Robustness
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Lack of theoretical framework for high-risk suicidal intent analysis
• Understanding language patterns in suicidal ideation posts and responses
• Evaluating AI chatbots' effectiveness in empathetic mental health support

Innovation

• Used Interpersonal Theory of Suicide for analysis
• Analyzed Reddit posts with machine learning
• Evaluated AI chatbot support limitations