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

Cyberbullying (CB) presents a pressing threat, especially to children, underscoring the urgent need for robust detection systems to ensure online safety. While large-scale datasets on online abuse exist, there remains a significant gap in labeled data that specifically reflects the language and communication styles used by children. The acquisition of such data from vulnerable populations, such as children, is challenging due to ethical, legal and technical barriers. Moreover, annotating these datasets relies heavily on human effort, which not only strains resources but also raises significant concerns due to annotators’ exposure to harmful content. In this paper, we address these challenges by leveraging Large Language Models (LLMs) to generate synthetic data and labels. Our experiments demonstrate that synthetic data enables BERT-based CB classifiers to achieve performance close to that of those trained on fully authentic datasets (75.8% vs. 81.5% accuracy). Additionally, LLMs can effectively label authentic yet unlabeled data, allowing BERT classifiers to attain a comparable performance level (79.1% vs. 81.5% accuracy). These results highlight the potential of LLMs as a scalable, ethical, and cost-effective solution for generating data for CB detection.

We propose the use of WordNet synsets in a syntax-based reordering model for hierarchical statistical machine translation (HPB-SMT) to enable the model to generalize to phrases not seen in the training data but that have equivalent meaning. We detail our methodology to incorporate synsets’ knowledge in the reordering model and evaluate the resulting WordNet-enhanced SMT systems on the English-to-Farsi language direction. The inclusion of synsets leads to the best BLEU score, outperforming the baseline (standard HPB-SMT) by 0.6 points absolute.