Examining the Efficacy of Multi-Theoretical Social Science-informed Deep Learning Models in Predicting Mob Outcomes

Organized and coordinated events, whether conducted in physical spaces (e.g., fun flash mobs, parkour, or even deviant mobs), cyberspace (e.g., collective hacking, organized propaganda campaigns), or in both spaces (e.g., electronic to face-to-face (e2f) events), represent various forms of collective action aimed at improving a group's status or condition and usually to achieve a common goal. Understanding such events requires a combination of technological and sociological approaches due to the complexity of the relationships that could exist, form, and dissolve among participating individuals. In this research, we integrate our knowledge of five social science theories that can explain such events with technical skills. We use this combination to estimate theoretical factors (both event-related and individual-related) using data collected from Meetup.com. We then train four classifiers using a deep neural network to predict the mob's outcome and rank the importance of each factor in determining the mob outcome. Results suggest that using factors related to individuals and aggregated per event made the model better classify mob outcomes (success or failure) than using event-related factors. Also, combining these factors did not affect the performance. However, all models performed better than the base model, which used raw data.