Scalable and timely detection of cyberbullying in online social networks.

Cyberbullying in Online Social Networks (OSNs) has grown to be a serious problem among teenagers. While a considerable amount of research has been conducted focusing on designing highly accurate classifiers to automatically detect cyberbullying instances in OSNs, two key practical issues remain to be worked upon, namely scalability of a cyberbullying detection system and timeliness of raising alerts whenever cyberbullying occurs. These two issues form the motivation of our work. We propose a multi-stage cyberbullying detection solution that drastically reduces the classification time and the time to raise alerts. The proposed system is highly scalable without sacrificing accuracy and highly responsive in raising alerts. The design is comprised of two novel components, a dynamic priority scheduler and an incremental classification mechanism. We have implemented this solution, and using data obtained from Vine, we conducted a thorough performance evaluation to demonstrate the utility and scalability of each of these components. We show that our complete solution is significantly more scalable and responsive than the current state of the art.