Detecting Cyberbullying Activity Across Platforms

Abstract

This article focuses on cyber security issues related to content. Recent surveys show that youth are being exposed to cyber-aggression at increasing rates, with over 43% of youth reporting in one recent survey that they have been bullied online. While research into this problem has been growing, the research community is hampered by a lack of authentic data for studying communication with and among youth. A large corpus with 800,000 instances of cell phone textual data from youth ages 10–14 has been developed to address this need. This article describes the dataset, as well as plans to enable access to the data while protecting the privacy of the study participants. The results from machine learning experiments for the detection of cyberbullying based on labeled data from several sources, including both SMS and social media messages, are also discussed. These algorithms are shown to be effective at detecting cyberbullying across platforms.