@inproceedings{verma-etal-2025-bullybench,
title = "{B}ully{B}ench: Youth {\&} Experts-in-the-loop Framework for Intrinsic and Extrinsic Cyberbullying {NLP} Benchmarking",
author = "Verma, Kanishk and
Kalaivendan, Sri Balaaji Natarajan and
Kazemi, Arefeh and
Wagner, Joachim and
McCashin, Darragh and
Walsh, Isobel and
Basak, Sayani and
Asci, Sinan and
Cherkasova, Yelena and
Poullis, Alexandrous and
O{'}Higgins Norman, James and
Umbach, Rebecca and
Milosevic, Tijana and
Davis, Brian",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.152/",
doi = "10.18653/v1/2025.emnlp-industry.152",
pages = "2172--2208",
ISBN = "979-8-89176-333-3",
abstract = "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 \textbf{youth-in-the-loop} dataset ``\textbf{BullyBench}'' developed by adolescents (ages 15{--}16) through an ethical co-research framework. We introduce a structured \textbf{intrinsic} quality evaluation with \textbf{experts-in-the-loop} (social scientists, psychologists, and content moderators) for assessing realism, relevance, and coherence in youth CB data. Additionally, we perform \textbf{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"
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<abstract>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</abstract>
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%0 Conference Proceedings
%T BullyBench: Youth & Experts-in-the-loop Framework for Intrinsic and Extrinsic Cyberbullying NLP Benchmarking
%A Verma, Kanishk
%A Kalaivendan, Sri Balaaji Natarajan
%A Kazemi, Arefeh
%A Wagner, Joachim
%A McCashin, Darragh
%A Walsh, Isobel
%A Basak, Sayani
%A Asci, Sinan
%A Cherkasova, Yelena
%A Poullis, Alexandrous
%A O’Higgins Norman, James
%A Umbach, Rebecca
%A Milosevic, Tijana
%A Davis, Brian
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F verma-etal-2025-bullybench
%X 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
%R 10.18653/v1/2025.emnlp-industry.152
%U https://aclanthology.org/2025.emnlp-industry.152/
%U https://doi.org/10.18653/v1/2025.emnlp-industry.152
%P 2172-2208
Markdown (Informal)
[BullyBench: Youth & Experts-in-the-loop Framework for Intrinsic and Extrinsic Cyberbullying NLP Benchmarking](https://aclanthology.org/2025.emnlp-industry.152/) (Verma et al., EMNLP 2025)
ACL
- Kanishk Verma, Sri Balaaji Natarajan Kalaivendan, Arefeh Kazemi, Joachim Wagner, Darragh McCashin, Isobel Walsh, Sayani Basak, Sinan Asci, Yelena Cherkasova, Alexandrous Poullis, James O’Higgins Norman, Rebecca Umbach, Tijana Milosevic, and Brian Davis. 2025. BullyBench: Youth & Experts-in-the-loop Framework for Intrinsic and Extrinsic Cyberbullying NLP Benchmarking. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2172–2208, Suzhou (China). Association for Computational Linguistics.