Abstract

Gun violence remains a critical societal challenge, demanding innovative predictive and preventative measures (Pew Research Center, 2025). This research proposes a novel Information Systems (IS) approach leveraging real-time social media signals from Twitter to predict gun violence incidents by integrating spatiotemporal analytics with contextual socioeconomic and demographic attributes. Drawing upon theories of social informatics, big data analytics, and socio-technical systems (STS), this study will collect and analyze geolocated tweets around reported gun violence incidents, employing advanced Natural Language Processing (NLP) and machine learning techniques, such as sentiment analysis and topic modeling, to detect linguistic patterns predictive of potential violence. The research further extends previous work by Mansoor and Ansari (2024) on predictive social media analytics for mental health crises and applies similar methodologies to gun violence. Additionally, building on spatial crime analysis methodologies described in "Three Decades of GIS Application in Spatial Crime Analysis" (2023), this study will use Geographic Information Systems (GIS) to map and analyze spatial distributions and patterns. The integration of publicly available socioeconomic and demographic data, such as income inequality, education levels, and population density, will enhance the predictive capabilities of the model, inspired by recent findings by Tiikkaja, Tindberg, and Durbeej (2024), who emphasized the influence of socioeconomic factors on violence exposure. The integrated analytical framework aims to identify statistically significant correlations among social media signals, geographic patterns, and socioeconomic contexts in predicting gun violence occurrences. Anticipated outcomes include creating a robust predictive model that identifies high-risk neighborhoods and critical timeframes, guiding targeted interventions, strategic policing, and community-driven violence prevention programs. Furthermore, this study explores potential applications for real-time alert systems, policy formulation, and resource optimization. This research contributes significantly to the IS body of knowledge by demonstrating the synergistic potential of social media analytics, geospatial technologies, and socioeconomic data in proactively addressing gun violence, thus providing a replicable, scalable, and evidence-based model for societal violence prevention.

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