Real-time Aggression Detection on Social Media
The rise of online aggression on social media is evolving into a major point of concern. Several machine and deep learning approaches have been proposed recently for detecting various types of aggressive behavior. However, social media are fast paced, generating an increasing amount of content, while aggressive behavior evolves over time. We introduce the first practical, real-time framework for detecting aggression on Twitter by embracing the streaming machine-learning paradigm. The framework is designed to be adaptable (its ML classifiers are trained incrementally as they receive new annotated examples), scalable (it can process the entire Twitter Firehose with three machines), and generalizable (it can detect other abusive behaviors such as sarcasm, racism, and sexism in real time). This project is done in collaboration with Dr Nicolas Kourtellis from Telefonica Research, Spain and Dr Despoina Chatzakou from Centre for Research and Technology Hellas, Greece.
