Abstract: Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. We model this behavior using social network and language-based features, which improve classifier performance. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.

Introduction: Cyberbullying poses a serious threat to the safety of online communities. The Centers for Disease Control and Prevention (CDC) identify cyberbullying as a “growing public health problem in need of additional research and prevention efforts” BIBREF0. Cyberbullying has been linked to negative mental health outcomes, including depression, anxiety, and other forms of self-harm, suicidal ideation, suicide attempts, and difficulties with social and emotional processing BIBREF1, BIBREF2, BIBREF3. Where traditional bullying was once limited to a specific time and place, cyberbullying can occur at any hour and from any location on earth BIBREF4. Once the first message has been sent, the attack can escalate rapidly as harmful content is spread across shared media, compounding these negative effects BIBREF5, BIBREF6. Internet users depend on content moderators to flag abusive text and ban cyberbullies from participating in online communities. However, due to the overwhelming volume of social media data produced every day, manual human moderation is often unfeasible. For this reason, social media platforms are beginning to rely instead on machine learning classifiers for automatic cyberbullying detection BIBREF7. The research community has developed increasingly competitive classifiers to detect harmful or aggressive content in text. Despite significant progress in recent years, however, existing models remain unfit for real-world applications. This is due, in part, to shortcomings in the training and testing data BIBREF8, BIBREF9, BIBREF10. Most annotation schemes have ignored the importance of social context, and researchers have neglected to provide annotators with objective criteria for distinguishing cyberbullying from other crude messages. To address the urgent need for reliable data, we provide an original annotation framework and an annotated Twitter dataset. The key advantages to our labeling approach are: [leftmargin=.2in] Contextually-informed ground truth. We provide annotators with the social context surrounding each message, including the contents of the reply thread and the account information of each user involved. Clear labeling criteria. We ask annotators to provide labels for five clear cyberbullying criteria. These criteria can be combined and adapted for revised definitions of cyberbullying. Using our new dataset, we experiment with existing NLP features and compare results with a newly-proposed set of features. We designed these features to encode the dynamic relationship between a potential bully and victim, using comparative measures from their relative linguistic and social network profiles. Additionally, our features have low computational complexity, so they can scale to internet-scale datasets, unlike expensive network centrality and clustering measurements. Results from our experiments suggest that, although existing NLP models can reliably detect aggressive language in text, these lexically-trained classifiers will fall short of the more subtle goal of cyberbullying detection. With $n$-grams and dictionary-based features, classifiers prove unable to detect harmful intent, visibility among peers, power imbalance, or the repetitive nature of aggression with sufficiently high precision and recall. However, our proposed feature set improves $F_1$ scores on all four of these social measures. Real-world detection systems can benefit from our proposed approach, incorporating the social aspects of cyberbullying into existing models and training these models on socially-informed ground truth labels.

Question: What social-network features are used?
Answer: Downward overlap, upward overlap, inward overlap, outward overlap, bidirectional overlap, count of friends of each user, count of followers of each user, users verified status, number of tweets posted within six-month snapshots