# Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces arXiv: https://arxiv.org/abs/2504.13277 ยท HTML: https://mlsanity.com/paper/2504.13277/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 155 references that are in the map. - First posted on arXiv 17 Apr 2025. ## Builds on (most cited first, 21 of 21) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020) - [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022) - [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018) - [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020) - [The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships](https://mlsanity.com/paper/2410.20130/) (2024) - [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024) - [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023) - [Modeling Empathy and Distress in Reaction to News Stories](https://mlsanity.com/paper/1808.10399/) (2018) - [Benefits and Harms of Large Language Models in Digital Mental Health](https://mlsanity.com/paper/2311.14693/) (2023) - [Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models](https://mlsanity.com/paper/2309.08902/) (2023) - [Don't lose the message while paraphrasing: A study on content preserving style transfer](https://mlsanity.com/paper/2308.09055/) (2023) - [Bias in Generative AI](https://mlsanity.com/paper/2403.02726/) (2024) - [Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use](https://mlsanity.com/paper/2410.19155/) (2024) - [An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives](https://mlsanity.com/paper/2310.17017/) (2023) - [Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders](https://mlsanity.com/paper/2303.06794/) (2023) - [The Effect of Moderation on Online Mental Health Conversations](https://mlsanity.com/paper/2005.09225/) (2020) - [LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts](https://mlsanity.com/paper/2306.05596/) (2023) - [Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries](https://mlsanity.com/paper/2504.09271/) (2025) - ["I Cannot Do All of This Alone": Exploring Instrumental and Prayer Support in Online Health Communities](https://mlsanity.com/paper/2005.11884/) (2020) - [Toward Safe Evolution of Artificial Intelligence (AI) based Conversational Agents to Support Adolescent Mental and Sexual Health Knowledge Discovery](https://mlsanity.com/paper/2404.03023/) (2024) ## Built on by (most cited first, 5 of 5) - [RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions](https://mlsanity.com/paper/2601.13235/) (2026) - [Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theory](https://mlsanity.com/paper/2509.22505/) (2025) - [AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?](https://mlsanity.com/paper/2603.19574/) (2026) - [Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media](https://mlsanity.com/paper/2510.14889/) (2025) - [LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback](https://mlsanity.com/paper/2605.30273/) (2026) ## Abstract (arXiv) 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.