arXiv Open Access 2023

Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media

Liam Hebert Gaurav Sahu Yuxuan Guo Nanda Kishore Sreenivas Lukasz Golab +1 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.CL cs.LG cs.MM cs.SI

Penulis (6)

Liam Hebert

Gaurav Sahu

Yuxuan Guo

Nanda Kishore Sreenivas

Lukasz Golab

Robin Cohen

Format Sitasi

APA MLA BibTeX

Hebert, L., Sahu, G., Guo, Y., Sreenivas, N.K., Golab, L., Cohen, R. (2023). Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media. https://arxiv.org/abs/2307.09312

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓