In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics. In particular, cyberbullying affects more than half of young social media users worldwide, and has also led to teenage suicides, prompted by prolonged and or coordinated digital harassment. Nonetheless, tools and technologies for understanding and mitigating it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of cyberbullies and aggressors, and what features distinguish them from regular users. We find that bully users post less, participate in fewer online communities, and are less popular than normal users, while aggressors are quite popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, achieving over 90 AUC.
Sentiment analysis of text can also contribute useful features in detecting offensive or abusive content. For instance, @cite_18 use sentiment scores of data collected from Kongregate (online gaming site), Slashdot, and MySpace. They use a probabilistic sentiment analysis approach to distinguish between bullies and non-bullies, and rank the most influential users based on a predator-victim graph built from exchanged messages. @cite_10 rely on sentiment to identify victims on Twitter who pose high risk to themselves or others. Apart from using positive and negative sentiments, they consider specific emotions such as anger, embarrassment, and sadness. Finally, Patch @cite_43 studies the presence of such emotions (anger, sadness, fear) in bullying instances on Twitter.
@cite_43 @cite_18 @cite_10
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The rapid growth of social networking and gaming sites is associated with an increase of online bullying activities which, in the worst scenario, result in suicidal attempts by the victims. In this paper, we propose an effective technique to detect and rank the most influential persons (predators and victims). It simplifies the network communication problem through a proposed detection graph model. The experimental results indicate that this technique is highly accurate. Bullying is a serious national health issue among adolescents. Social media offers a new opportunity to study bullying in both physical and cyber worlds. Sentiment analysis has the potential to identify victims who pose high risk to themselves or others, and to enhance the scientific understanding of bullying overall. We identify seven emotions common in bullying. While some of the emotions are well-studied before, others are non-standard in the sentiment analysis literature. We propose a fast training procedure to recognize these emotions without explicitly producing a conventional labeled training dataset. We apply our procedure to social media posts on bullying and discuss our findings.