Bridging online behavior and clinical insight: A longitudinal LLM-based study of suicidality on YouTube reveals novel digital markers

Suicide remains a leading cause of death in Western countries. As social media becomes central to daily life, digital footprints offer valuable insight into suicidal behavior. Focusing on individuals who attempted suicide while uploading videos to their channels, we investigate: How do linguistic patterns on YouTube reflect suicidal behavior, and how do these patterns align with or differ from expert knowledge ? We examined linguistic changes around suicide attempts and compared individuals who attempted suicide while actively uploading to their channel with three control groups: those with prior attempts, those experiencing major life events, and matched individuals from the broader cohort. Applying complementary bottom-up, hybrid, and expert-driven approaches, we analyzed a novel longitudinal dataset of 181 suicide-attempt channels and 134 controls. 1 1 Data will be available upon ethics and legal approval, and under a protocol that ensures compliance with data protection and privacy regulations. In the bottom-up analysis, LLM-based topic-modeling identified 166 topics; five were linked to suicide attempts, two also showed attempt-related temporal changes ( Mental Health Struggles , OR = 1.74; YouTube Engagement , OR = 1.67; p < .01). 2 2 Odds ratios (OR) quantify the change in the odds of topic prevalence after versus before the suicide attempt within the group of YouTubers who attempted suicide. In the hybrid approach, clinical experts reviewed LLM-derived topics and flagged 19 as suicide-related. However, none showed significant effects beyond those identified bottom-up. YouTube Engagement , a platform-specific indicator, was not flagged, underscoring the value of bottom-up discovery. A top-down psychological assessment of suicide narratives revealed differing motivations: individuals describing prior attempts aimed to help others ( β = 1.69, p < .01), whereas those attempted during the uploading period emphasized personal recovery ( β = 1.08, p < .01). By integrating these approaches, we offer a nuanced understanding of suicidality, bridging digital behavior and clinical insights. • Studied 181 attempt-related and 134 control YouTube channels. • LLM-based topic modeling revealed suicide-linked behavioral markers. • Detected temporal shifts in mental health and engagement topics. • Expert review missed platform-specific signal found bottom-up. • Past attempters aimed to help others; recent attempters focused on recovery.