Trustworthy Hate Speech Detection Through Visual Augmentation
This addresses hate speech detection for social media platforms, but it appears incremental as it builds on existing multimodal approaches.
The paper tackles hate speech detection on social media by proposing TrusV-HSD, a method that integrates visual augmentation and trustworthy loss to address uncertainty, resulting in remarkable improvements over conventional methods on public datasets.
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.