Exploring the contagion effect of social media on mass shootings

Abstract

Social media plays a prominent role in the spread of mass shootings. It brought about a significant contagious effect on future similar incidents. Therefore, we explore Machine Learning (ML) models to forecast the change in the public’s attitudes about mass shootings on social media over time. These ML models include Support Vector Machine (SVM), Logistic Regression (LR), and the optimized Deep Neural Networks based on an Improved Particle Swarm Optimization algorithm (IPSO-DNN). We then propose a self-excited contagion model to predict the number of mass shootings by focusing on the spread of public attitudes on Twitter. Moreover, we also improve the proposed contagion model with the consideration of social distancing and the daily growth rate of COVID-19 cases, to predict and analyze mass shootings under the COVID-19 pandemic. Experimental results demonstrate that the proposed contagion models perform very well in predicting future mass shootings in the United States.