Abstract-Cyberbullying is the use of technology as a medium to bully someone. Although it has been an issue for many years, the recognition of its impact on young people has recently increased. Social networking sites provide a fertile medium for bullies, and teens and young adults who use these sites are vulnerable to attacks. Through machine learning, we can detect language patterns used by bullies and their victims, and develop rules to automatically detect cyberbullying content.The data we used for our project was collected from the website Formspring.me, a question-and-answer formatted website that contains a high percentage of bullying content. The data was labeled using a web service, Amazon's Mechanical Turk. We used the labeled data, in conjunction with machine learning techniques provided by the Weka tool kit, to train a computer to recognize bullying content. Both a C4.5 decision tree learner and an instance-based learner were able to identify the true positives with 78.5% accuracy.

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Cyberbullying is a relatively new phenomenon associated with the widespread adoption of various digital communication technologies, including the internet and mobile phones. As of 2013, nearly 20% of youths in grades 9-12 in the US reported being traditionally bullied in face-to-face encounters while almost 15% reported being cyberbullied (Kann et al., 2014). Bullying victimization is associated with a variety of behavioral and psychological effects, from becoming bullies themselves (i.e., bully-victims), to poor academic performance, depression and suicidal ideation (Nansel et al., 2001;Wang, Nansel, & Iannotti, 2011;Willard, 2007). Research on these phenomena has focused primarily on white youth, leaving a void in our understanding of how cyberbullying has affected youth of color. This narrative literature review addresses this oversight by providing an overview of recent cyberbullying research that focuses on Hispanic, Asian and black adolescents (k=15). We found that youth of color appear to be less likely to experience cyberbullying than white youth but they experience suicidal ideation and attempts at about the same rates when they do experience cyberbullying.

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In this paper we describe a close analysis of the language used in cyberbullying. We take as our corpus a collection of posts from Formspring.me. Formspring.me is a social networking site where users can ask questions of other users. It appeals primarily to teens and young adults and the cyberbullying content on the site is dense; between 7% and 14% of the posts we have analyzed contain cyberbullying content.The results presented in this article are two-fold. Our first experiments were designed to develop an understanding of both the specific words that are used by cyberbullies, and the context surrounding these words. We have identified the most commonly used cyberbullying terms, and have developed queries that can be used to detect cyberbullying content. Five of our queries achieve an average precision of 91.25% at rank 100.In our second set of experiments we extended this work by using a supervised machine learning approach for detecting cyberbullying. The machine learning experiments identify additional terms that are consistent with cyberbullying content, and identified an additional querying technique that was able to accurately assign scores to posts from Formspring.me. The posts with the highest scores are shown to have a high density of cyberbullying content.