Cyberbullying (CB) involves complex relational dynamics that are often oversimplified as a binary classification task. Existing youth-focused CB datasets rely on scripted role-play, lacking conversational realism and ethical youth involvement, with little or no evaluation of their social plausibility. To address this, we introduce a youth-in-the-loop dataset “BullyBench” developed by adolescents (ages 15–16) through an ethical co-research framework. We introduce a structured intrinsic quality evaluation with experts-in-the-loop (social scientists, psychologists, and content moderators) for assessing realism, relevance, and coherence in youth CB data. Additionally, we perform extrinsic baseline evaluation of this dataset by benchmarking encoder- and decoder-only language models for multi-class CB role classification for future research. A three-stage annotation process by young adults refines the dataset into a gold-standard test benchmark, a high-quality resource grounded in minors’ lived experiences of CB detection. Code and data are available for review

In the digital age, cyberbullying (CB) poses a significant concern, impacting individuals as early as primary school and leading to severe or lasting consequences, including an increased risk of self-harm. CB incidents, are not limited to bullies and victims, but include bystanders with various roles, and usually have numerous sub-categories and variations of online harms. This position paper emphasises the complexity of CB incidents by drawing on insights from psychology, social sciences, and computational linguistics. While awareness of CB complexities is growing, existing computational techniques tend to oversimplify CB as a binary classification task, often relying on training datasets that capture peripheries of CB behaviours. Inconsistent definitions and categories of CB-related online harms across various platforms further complicates the issue. Ethical concerns arise when CB research involves children to role-play CB incidents to curate datasets. Through multi-disciplinary collaboration, we propose strategies for consideration when developing CB detection systems. We present our position on leveraging large language models (LLMs) such as Claude-2 and Llama2-Chat as an alternative approach to generate CB-related role-playing datasets. Our goal is to assist researchers, policymakers, and online platforms in making informed decisions regarding the automation of CB incident detection and intervention. By addressing these complexities, our research contributes to a more nuanced and effective approach to combating CB especially in young people.