mlsanityData to 7 Oct 2026

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

arXiv 2307.09312 ↗First posted 18 Jul 2023cs.CL cs.LG cs.MMcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 18 Jul 2023.

Builds on

25 of 43 references.

References · 25

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  7. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,224
  8. 2020The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memescited by 485
  9. 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 403
  10. 2021Attention Bottlenecks for Multimodal Fusioncited by 361
  11. 2019Predicting the Type and Target of Offensive Posts in Social Mediacited by 268
  12. 2018Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviorcited by 265
13 more
  1. 2020Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detectioncited by 217
  2. 2020HateBERT: Retraining BERT for Abusive Language Detection in Englishcited by 206
  3. 2019A Benchmark Dataset for Learning to Intervene in Online Hate Speechcited by 128
  4. 2019A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Mediacited by 95
  5. 2023Prompting for Multimodal Hateful Meme Classificationcited by 68
  6. 2020Quantifying social organization and political polarization in online platformscited by 30
  7. 2022Understanding Political Polarization via Jointly Modeling Users, Connections and Multimodal Contents on Heterogeneous Graphscited by 8
  8. 2023Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Contextcited by 8
  9. 2021Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attentioncited by 5
  10. 2022TweetNERD -- End to End Entity Linking Benchmark for Tweetscited by 4
  11. 2021Towards A Multi-agent System for Online Hate Speech Detectioncited by 2
  12. 2023Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Contentcited by 2
  13. 2022Robust Candidate Generation for Entity Linking on Short Social Media Textscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025A Survey of Graph Transformers: Architectures, Theories and Applicationscited by 12
  2. 2024Trustworthy Hate Speech Detection Through Visual Augmentationcited by 2
  3. 2025Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insightscited by 1
  4. 2026MEMEWEAVER: Inter-Meme Graph Reasoning for Sexism and Misogyny Detectioncited by 1
  5. 2025Co-AttenDWG: Co-Attentive Dimension-Wise Gating and Expert Fusion for Multi-Modal Offensive Content Detectioncited by 0
  6. 2026Community Norms in the Spotlight: Enabling Task-Agnostic Unsupervised Pre-Training to Benefit Online Social Mediacited by 0