Trustworthy Hate Speech Detection Through Visual Augmentation
<|reference_start|>Trustworthy Hate Speech Detection Through Visual Augmentation: The surge of hate speech on social media platforms poses a significant challenge, with hate speech detection~(HSD) becoming increasingly critical. Current HSD methods focus on enriching contextual information to enhance detection performance, but they overlook the inherent uncertainty of hate speech. We propose a novel HSD method, named trustworthy hate speech detection method through visual augmentation (TrusV-HSD), which enhances semantic information through integration with diffused visual images and mitigates uncertainty with trustworthy loss. TrusV-HSD learns semantic representations by effectively extracting trustworthy information through multi-modal connections without paired data. Our experiments on public HSD datasets demonstrate the effectiveness of TrusV-HSD, showing remarkable improvements over conventional methods.<|reference_end|>
@article{yang2024trustworthy,
    title={Trustworthy Hate Speech Detection Through Visual Augmentation},
    author={Ziyuan Yang and Ming Yan and Yingyu Chen and Hui Wang and Zexin Lu and
  Yi Zhang},
    journal={arXiv preprint arXiv:2409.13557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13557},
    primaryClass={cs.CV cs.AI}
}