Enhancing implicit hate speech detection via LLM-generated adversarial samples

Lang Zhang (), Hongtao Deng (), Wang Gao (), Yang Yu () and Rui Xu ()
Additional contact information
Lang Zhang: Jianghan University
Hongtao Deng: Jianghan University
Wang Gao: Jianghan University
Yang Yu: Jianghan University
Rui Xu: Jianghan University

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 12, No 10, 3994-4006

Abstract: Abstract As the amount of hate speech on social media increases, the demand for its automatic detection grows more urgent. Current research primarily focuses on the detection of explicit hate speech, while the detection of more subtle and implicit forms of hate speech remains a significant challenge. These covert types of hate speech are often not easily recognized by standard classifiers due to their less obvious pragmatic and semantic features. In this paper, we propose a novel framework, adversarial implicit hate speech generator (AIHSG), which utilizes large language models to generate adversarial, implicit hate speech short text messages. These samples may not contain evident signs of hate speech on the surface but convey hateful intent through context and metaphor. The generated adversarial samples undergo preliminary manual screening to ensure that they align with the characteristics of implicit hate speech. The AIHSG-generated adversarial samples are then employed to augment the training data of supervised learning models, enhancing their performance on implicit hate speech detection tasks. Our experimental results demonstrate the effectiveness of this approach in improving model capabilities for detecting implicit hate speech.

Keywords: Implicit hate speech detection; Large language model; Adversarial sample; Constrained beam search (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-025-02909-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:16:y:2025:i:12:d:10.1007_s13198-025-02909-y

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-025-02909-y

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().