MMHS: Multimodal Model for Hate Speech Intensity Prediction (Speech and Computer, Springer)

By normalizing hate speech and maintaining a balance between responsible communication and free expression, it is essential to accurately gauge the severity of hate speech in order to moderate and regulate large online groups. The method at hand creates the new Multimodal Model for Hate Speech (MMHS) by utilizing knowledge from both the linguistic and visual domains. By achieving 0.350 lower Root Mean Squared Error (RMSE) and 0.132 and 0.012 higher Pearson and Cosine metrics, respectively, MMHS outperforms prior benchmarks and demonstrates state-of-the-art performance on the NACL dataset. Furthermore, user preference polls show that our forecasts are significantly preferred by 16.67% above those of Masud et al. By improving knowledge of online conversation and advancing the technological landscape of hate speech identification, the study makes it possible to implement more effective moderation techniques.

https://link.springer.com/chapter/10.1007/978-3-031-78014-1_8