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Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge

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arxiv 2012.12975 v1 pith:44BQB7IS submitted 2020-12-23 cs.AI cs.CLcs.CV

Riza Velioglu , Jewgeni Rose This is my paper
classification cs.AIcs.CLcs.CV
keywords memeschallengehatefulmultimodalhatespeechdetectingharmless
verification ladder T0 review T1 audit T2 compute T3 formal
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Memes on the Internet are often harmless and sometimes amusing. However, by using certain types of images, text, or combinations of both, the seemingly harmless meme becomes a multimodal type of hate speech -- a hateful meme. The Hateful Memes Challenge is a first-of-its-kind competition which focuses on detecting hate speech in multimodal memes and it proposes a new data set containing 10,000+ new examples of multimodal content. We utilize VisualBERT -- which meant to be the BERT of vision and language -- that was trained multimodally on images and captions and apply Ensemble Learning. Our approach achieves 0.811 AUROC with an accuracy of 0.765 on the challenge test set and placed third out of 3,173 participants in the Hateful Memes Challenge.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MemeScouts@LT-EDI 2026: Asking the Right Questions -- Prompted Weak Supervision for Meme Hate Speech Detection

    cs.CL 2026-04 unverdicted novelty 6.0 of 10

    Prompted weak supervision via question-based labeling functions with a VLM outperforms direct classification for multilingual meme hate speech detection and ranks highly in the LT-EDI 2026 task.

  2. Unpacking Hateful Memes: Presupposed Context and False Claims

    cs.CL 2025-10 conditional novelty 6.0 of 10

    A hateful-meme detector that combines presupposed-context fusion, LLM-based social perception, and cross-modal reference graphs outperforms prior models on three benchmarks and transfers to fake news.

  3. MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection

    cs.CL 2025-07 conditional novelty 6.0 of 10

    MIND uses unlabeled similar memes, bidirectional AI insight derivation, and multi-agent debate to improve zero-shot harmful meme detection on HarM, FHM, and MAMI.

  4. Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities

    cs.CL 2024-06 accept novelty 6.0 of 10

    A PRISMA-based survey of 158 computational works on toxic meme detection introduces a new toxicity taxonomy and a framework linking target, intent, and conveyance tactics while noting trends in LLMs and cross-modal methods.

  5. RMS@CC-MMD 2026: Multimodal Misogyny Detection via Geometric Interaction and Multi-View Consensus

    cs.CV 2026-07 conditional novelty 5.0 of 10

    GeoMVC, using frozen CLIP/mCLIP with Hadamard-plus-cosine fusion and multi-view majority voting, ranks 2nd/3rd on Malayalam/Chinese misogyny-meme detection but struggles on Tamil.

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