VISAGE: Detection and automatic classification of urban violence through social media data

Urban violence remains a challenge for cities, requiring innovative approaches to provide actionable insights for authorities. This study presents VISAGE, a framework designed to detect and classify urban violence in social media textual data using an extension of the Quantification of Violence Scale (QoVS). Ten Machine learning models, including BERT and Random Forest, were evaluated on Twitter datasets from violent events in Brazil, with BERT achieving an F1-score of 0.86. Results demonstrate the feasibility of automating violence assessment from text. Limitations like dataset imbalance and labeling process are discussed with future work targeting real-time and multimodal analysis.