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The goal of the present study was to evaluate the Theory of Planned Behavior (TPB) as an explanation for bystanders’ intention to help cyberbullying victims among college students. Participants completed an online survey in which their intention, attitude, subjective norm, and perceived behavioral control toward helping cyberbullying victims were assessed. In addition to these traditional TPB variables, empathy toward cyberbullying victims and anticipated regret from not helping victims were included in the model. Results showed that empathy and anticipated regret significantly predicted intention to help cyberbullying victims over and above the traditional TPB variables. Results also showed that gender altered how traditional TPB variables, empathy, and anticipated regret predict bystander’s intention to help cyberbullying victims: Empathy and anticipated regret were most robust predictors for males and females, respectively. These results suggest that the TPB is a useful theoretical framework for understanding bystanders’ intention to help cyberbullying victims. Implications for developing effective prevention and intervention strategies are discussed.

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Cyberbullying is a major cyber issue that is common among adolescents. Recent reports show that more than one out of five students in the United States is a victim of cyberbullying. Majority of cyberbullying incidents occur on public social media platforms such as Twitter. Automated cyberbullying detection methods can help prevent cyberbullying before the harm is done on the victim. In this study, we analyze two corpora of cyberbullying tweets from similar incidents to construct and validate an automated detection model. Our method emphasizes the two claims that are supported by our results. First, despite other approaches that assume that cyberbullying instances use vulgar or profane words, we show that they do not necessarily contain negative words. Second, we highlight the importance of context and the characteristics of actors involved and their position in the network structure in detecting cyberbullying rather than only considering the textual content in our analysis.