Can Social Media Tell Us Who Will Kill? Digital Warning Signs and the Problem of Predicting Rare Violence

Abstract

Digital traces often look persuasive after lethal attacks because the outcome gives earlier posts a meaning they did not necessarily have when first encountered. Threats, grievance, fixation, admiration for earlier attackers, and planning language can matter, but their presence does not solve the harder task of identifying a future offender before violence occurs. This article examines that problem through a qualitative integrative review. Evidence from mass public shootings, targeted violence, threat assessment, computational language analysis, and adjacent risk research shows that digital warning behavior is most informative when it develops as a changing trajectory and converges with preparation outside the platform. Yet retrospective offender studies select cases after the outcome is known, while prospective systems confront a low base rate in which many people display fragments of the same pattern and never commit lethal violence. This tension is conceptualized as the digital prediction paradox. Social media is therefore more defensible as a source of behavioral threat information and triage than as an instrument for assigning individual probabilities of homicide, with important implications for automated detection, policing, privacy, fairness, and proportionate prevention.