Table 4 Findings of the proposed method.
From: A comprehensive framework for multi-modal hate speech detection in social media using deep learning
S. no | Aspects | MHSDF | Ratio (%) |
|---|---|---|---|
1 | Detection accuracy | Effectiveness in identifying hate speech across text, images, audio, and video. | 98.53% |
2 | Robustness | Ability to handle diverse and complex hate speech scenarios, maintaining high detection accuracy. | 97.64% |
3 | Interpretability | Clarity in the model’s decision-making process, enhancing user confidence and understanding. | 97.71% |
4 | Scalability | Capability to process large volumes of multi-modal data efficiently across various platforms. | 98.67% |
5 | Performance | Overall effectiveness in detecting complex forms of hate speech, surpassing traditional methods. | 99.21% |
