ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos (2024.findings-acl)
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| Challenge: | Using a dataset of 931 videos with 4021 code-mixed Hindi-English utterances, we find that video content with multiple modalities is more accurate and more accurate than textual content. |
| Approach: | They propose to use a dataset to analyze toxic content in video content in non-English languages by leveraging language models. |
| Outcome: | The proposed framework achieves an Accuracy and Weighted F1 score of 94.29% and 94.35% for the first time in its class. |
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