Characterizing Online Activities Contributing to Suicide Mortality among Youth

TL;DR

This study analyzes online activities linked to youth suicide by examining death investigation reports, developing a thematic framework, and modeling these themes at scale to inform targeted interventions.

Contribution

It introduces a novel mixed-methods approach combining thematic analysis with zero-shot learning to identify and model online risk factors for youth suicide.

Findings

Identified 12 online activity themes related to suicide risk.

Observed increased prevalence of certain themes during COVID-19 lockdowns.

Linked online activity themes to decedent characteristics and suicide theories.

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.