Detecting Gang-Involved Escalation on Social Media Using Context
This work addresses the challenge of preventing gang-related violence by identifying escalation patterns in social media, though it is incremental as it builds on existing CNN methods with added context.
The paper tackles the problem of detecting aggression and loss in social media posts from gang-involved youth to predict real-world violence, achieving a significant improvement in detection accuracy by incorporating contextual representations into a Convolutional Neural Network.
Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user's recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.