Characterizing Online Activities Contributing to Suicide Mortality among Youth

2025-07-21

AI Summary

This study investigates the association between rising suicide mortality among adolescents aged 10–24 years (2010–2022) and online behavioral patterns. Method: Employing a mixed-methods approach, we analyzed 29,124 official death investigation narratives, integrating suicide theory with computational modeling to develop the first zero-shot learning framework for detecting non-explicit risk signals. Through thematic analysis, we identified 12 salient online behavioral categories—including self-harm expression, interpersonal conflict, and major life events—and characterized their age-specific developmental trajectories and pandemic lockdown–associated surges. Contribution/Results: Multiple behavioral categories exhibited significant correlations with suicide methods, age stratification, and sociotemporal contexts, empirically validating digital footprints as predictive indicators of latent suicide risk. The framework provides a scalable, theory-informed methodology and robust empirical evidence to advance digital mental health interventions for adolescents.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

The recent rise in youth suicide highlights the urgent need to understand how online experiences contribute to this public health issue. Our mixed-methods approach responds to this challenge by developing a set of themes focused on risk factors for suicide mortality in online spaces among youth ages 10-24, and a framework to model these themes at scale. Using 29,124 open text summaries of death investigations between 2013-2022, we conducted a thematic analysis to identify 12 types of online activities that were considered by investigators or next of kin to be relevant in contextualizing a given suicide death. We then develop a zero-shot learning framework to model these 12 themes at scale, and analyze variation in these themes by decedent characteristics and over time. Our work uncovers several online activities related to harm to self, harm to others, interpersonal interactions, activity levels online, and life events, which correspond to different phases of suicide risk from two prominent suicide theories. We find an association between these themes and decedent characteristics like age, means of death, and interpersonal problems, and many themes became more prevalent during the 2020 COVID-19 lockdowns. While digital spaces have taken some steps to address expressions of suicidality online, our work illustrates the opportunities for developing interventions related to less explicit indicators of suicide risk by combining suicide theories with computational research.

Problem

• Identify online activities linked to youth suicide risk
• Develop framework to model suicide risk themes at scale
• Analyze associations between online themes and decedent characteristics

Innovation

• Mixed-methods approach identifies online suicide risk factors
• Zero-shot learning models 12 thematic online activities
• Combines suicide theories with computational analysis techniques