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        <title>SCCD: A Session-based Dataset for Chinese Cyberbullying Detection</title>
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    <abstract>The rampant spread of cyberbullying content poses a growing threat to societal well-being. However, research on cyberbullying detection in Chinese remains underdeveloped, primarily due to the lack of comprehensive and reliable datasets. Notably, no existing Chinese dataset is specifically tailored for cyberbullying detection. Moreover, while comments play a crucial role within sessions, current session-based datasets often lack detailed, fine-grained annotations at the comment level. To address these limitations, we present a novel Chinese cyberbullying dataset, termed SCCD, which consists of 677 session-level samples sourced from a major social media platform Weibo. Moreover, each comment within the sessions is annotated with fine-grained labels rather than conventional binary class labels. Empirically, we evaluate the performance of various baseline methods on SCCD, highlighting the challenges for effective Chinese cyberbullying detection.</abstract>
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%0 Conference Proceedings
%T SCCD: A Session-based Dataset for Chinese Cyberbullying Detection
%A Yang, Qingpo
%A Chen, Yakai
%A Xu, Zihui
%A Shang, Yu-ming
%A Guo, Sanchuan
%A Zhang, Xi
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F yang-etal-2025-sccd
%X The rampant spread of cyberbullying content poses a growing threat to societal well-being. However, research on cyberbullying detection in Chinese remains underdeveloped, primarily due to the lack of comprehensive and reliable datasets. Notably, no existing Chinese dataset is specifically tailored for cyberbullying detection. Moreover, while comments play a crucial role within sessions, current session-based datasets often lack detailed, fine-grained annotations at the comment level. To address these limitations, we present a novel Chinese cyberbullying dataset, termed SCCD, which consists of 677 session-level samples sourced from a major social media platform Weibo. Moreover, each comment within the sessions is annotated with fine-grained labels rather than conventional binary class labels. Empirically, we evaluate the performance of various baseline methods on SCCD, highlighting the challenges for effective Chinese cyberbullying detection.
%U https://aclanthology.org/2025.coling-main.639/
%P 9533-9545

Markdown (Informal)

[SCCD: A Session-based Dataset for Chinese Cyberbullying Detection](https://aclanthology.org/2025.coling-main.639/) (Yang et al., COLING 2025)

• SCCD: A Session-based Dataset for Chinese Cyberbullying Detection (Yang et al., COLING 2025)

ACL

• Qingpo Yang, Yakai Chen, Zihui Xu, Yu-ming Shang, Sanchuan Guo, and Xi Zhang. 2025. SCCD: A Session-based Dataset for Chinese Cyberbullying Detection. In Proceedings of the 31st International Conference on Computational Linguistics, pages 9533–9545, Abu Dhabi, UAE. Association for Computational Linguistics.