Words matter when gangs cyberbang: Predicting imminent urban violence from gang members’ social media posts

Abstract

The rise in violent crime across major U.S. cities, fueled mainly by gang members using social media to broadcast messages of loss and aggression, poses an urgent challenge. Although prior research has examined gang-affiliated social media content, there remains a crucial gap in identifying which posts serve as credible signals of impending violence. Addressing this gap is essential for enhancing community safety, improving resource allocation, and optimizing law enforcement strategies. This study introduces a novel research model grounded in a contextualized adaptation of signaling theory. The model identifies key indicators of credible signals, such as follower count, specific hashtags, and retweet counts, which correlate with gang-related aggression. Environmental factors, such as temperature, are also examined for their influence on violent crime escalation. Using this contextualized theory, we designed a machine learning model to predict violent crime counts, training it on a dataset of 143,700 gang-affiliated tweets and their accompanying text and metadata. This approach enables automated identification of credible social media signals related to gang violence. The findings contribute to theory and practice by offering new insights into social media credibility and its link to violent crime, and by demonstrating how such signals can be used for prediction. Furthermore, the predictive model provides law enforcement with advanced tools to anticipate crime and inform community-based prevention strategies and policy development.