Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect
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Abstract (arXiv)
The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with ~2M posts by ~10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 2 of its 64 references that are in the map. First posted on arXiv 20 Feb 2023.
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2 of 64 references.
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Citing papers per year: 2024: 3; 2025: 3; 2026: 6.
12 papers.
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- 2025Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queriescited by 6
- 2026AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?cited by 5
- 2026The Hidden Toll of Social Media News: Causal Effects on Psychosocial Wellbeingcited by 4
- 2025Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Mediacited by 3
- 2026LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedbackcited by 2
- 2024Catalysts of Conversation: Examining Interaction Dynamics Between Topic Initiators and Commentors in Alzheimer's Disease Online Communitiescited by 0
- 2026Algorithmic Cultivation: How Social Media Feeds Shape User Languagecited by 0
- 2026Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptionscited by 0
- 2026Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions15 Sep 2026
- 2026LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback28 May 2026
- 2026Algorithmic Cultivation: How Social Media Feeds Shape User Language16 May 2026
- 2026AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?20 Mar 2026
- 2026The Hidden Toll of Social Media News: Causal Effects on Psychosocial Wellbeing20 Jan 2026
- 2026RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions19 Jan 2026
- 2025Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media16 Oct 2025
- 2025Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theory26 Sep 2025
- 2025Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries12 Apr 2025
- 2024Catalysts of Conversation: Examining Interaction Dynamics Between Topic Initiators and Commentors in Alzheimer's Disease Online Communities18 Dec 2024
- 2024AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworker18 Oct 2024
- 2024Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook10 Jun 2024