Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916133941018624 |
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| author | Hebert, Liam Sahu, Gaurav Guo, Yuxuan Sreenivas, Nanda Kishore Golab, Lukasz Cohen, Robin |
| author_facet | Hebert, Liam Sahu, Gaurav Guo, Yuxuan Sreenivas, Nanda Kishore Golab, Lukasz Cohen, Robin |
| contents | We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech involves a holistic analysis of text and images grounded in the discussion context. This is done by leveraging graph transformers to capture the contextual relationships in the discussion surrounding a comment and grounding the interwoven fusion layers that combine text and image embeddings instead of processing modalities separately. To evaluate our work, we present a new dataset, HatefulDiscussions, comprising complete multi-modal discussions from multiple online communities on Reddit. We compare the performance of our model to baselines that only process individual comments and conduct extensive ablation studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_09312 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media Hebert, Liam Sahu, Gaurav Guo, Yuxuan Sreenivas, Nanda Kishore Golab, Lukasz Cohen, Robin Computation and Language Machine Learning Multimedia Social and Information Networks We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech involves a holistic analysis of text and images grounded in the discussion context. This is done by leveraging graph transformers to capture the contextual relationships in the discussion surrounding a comment and grounding the interwoven fusion layers that combine text and image embeddings instead of processing modalities separately. To evaluate our work, we present a new dataset, HatefulDiscussions, comprising complete multi-modal discussions from multiple online communities on Reddit. We compare the performance of our model to baselines that only process individual comments and conduct extensive ablation studies. |
| title | Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media |
| topic | Computation and Language Machine Learning Multimedia Social and Information Networks |
| url | https://arxiv.org/abs/2307.09312 |