Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.13557 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914953792847872 |
|---|---|
| author | Yang, Ziyuan Yan, Ming Chen, Yingyu Wang, Hui Lu, Zexin Zhang, Yi |
| author_facet | Yang, Ziyuan Yan, Ming Chen, Yingyu Wang, Hui Lu, Zexin Zhang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trustworthy Hate Speech Detection Through Visual Augmentation Yang, Ziyuan Yan, Ming Chen, Yingyu Wang, Hui Lu, Zexin Zhang, Yi Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Trustworthy Hate Speech Detection Through Visual Augmentation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2409.13557 |