Under Construction

Abstract

As many children are connected to their peers and spend a significant amount of time on social web sites (Facebook, Twitter, etc.), cyber bullying in social media is becoming a severe problem that can lead to serious social, psychological, and health effects. To alleviate the problem, intervention from adults (teachers, parents, law enforcement, social web site moderators, etc.) is the key. However, many victims do not report bullying to adults, and bullies can use aliases and act anonymously, and thus they are difficult to identify. The goal of this exploratory project is to eliminate or at least reduce these problems by developing an intelligent system to automatically detect and track cyber bullies on the social web.

This project explores a solution to cyber bullying based on a combination of machine learning, natural language processing, information filtering, and recommendation and social network modeling techniques. The expected results of the project include: (1) algorithm(s) that can detect cyber bullies and bullying messages automatically; (2) piloting results that suggest what prediction accuracy to expect; (3) a preliminary social web bullying detection prototype system and (4) the first labeled social cyber-bullying data set for testing of the prototype and future research in this direction. The project has high risk, as whether such a system can be developed is an untested idea and the task is challenging, largely due to the diversity of bully behaviors, ambiguity, and the special language used by bullies in social media.