Harnessing Bullying Traces to Enhance Bullying Participant Role Identification in Multi-Party Chats

Abstract

As online content continues to grow, so does the spread of online hate, especially on social media. Most research efforts conducted on the task of bullying participant role identification are directed towards social networks such as Twitter and Instagram. However, private instant messaging platforms and channels were pinpointed in recent studies as the most prominent grounds for cyberbullying, especially among teens. Since data collection from major social media platforms is strictly limited, very few studies have investigated this task in a multi-party setting. However, the recent release of resources mimicking online aggression situations that may occur among teens on private instant messaging platforms contributes to filling this gap. In this study, we introduce a full pipeline aiming at automating the identification of bullying participant roles (bully and victim) in multi-party chats. Leveraging pre-trained language models and different learning frameworks, we perform hateful content classification of exchanged messages according to a binary scheme (online hate or no online hate). Then, - from these bullying traces - bullying behavioural cues (repetition and intention to harm) are derived and formalised into a role scoring function. As a result, the proposed pipeline identifies the bully and the victim among chat participants. Evaluated against state-of-the-art methods, the proposed pipeline achieves better performances considering all the datasets and roles to predict. In addition, the error analysis confirms that deriving bullying behavioural cues is beneficial to the task of participant role identification.