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A Multimodal Framework for the Detection of Hateful Memes
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An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes. Designing systems to automatically detect hateful content is of paramount importance if we are to mitigate its undesirable effects on the society at large. The detection of multimodal hate speech is an intrinsically difficult and open problem: memes convey a message using both images and text and, hence, require multimodal reasoning and joint visual and language understanding. In this work, we seek to advance this line of research and develop a multimodal framework for the detection of hateful memes. We improve the performance of existing multimodal approaches beyond simple fine-tuning and, among others, show the effectiveness of upsampling of contrastive examples to encourage multimodality and ensemble learning based on cross-validation to improve robustness. We furthermore analyze model misclassifications and discuss a number of hypothesis-driven augmentations and their effects on performance, presenting important implications for future research in the field. Our best approach comprises an ensemble of UNITER-based models and achieves an AUROC score of 80.53, placing us 4th on phase 2 of the 2020 Hateful Memes Challenge organized by Facebook.
Forward citations
Cited by 6 Pith papers
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Unpacking Hateful Memes: Presupposed Context and False Claims
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.
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MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection
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.
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MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models
A new 1,565-item benchmark shows large vision-language models classify meme-context relationships far better than they infer the poster's intent.
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RMS@CC-MMD 2026: Multimodal Misogyny Detection via Geometric Interaction and Multi-View Consensus
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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HCIG: A Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection
HCIG, a token/phrase/global graph network, reports 85.74% accuracy on MMSD and 69.62% on MultiBully, but its hierarchical gains over token-level-only and late-fusion baselines are small and inconsistently reported.
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Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing
A method that decomposes CLIP image and text features into a shared low-rank component and modality-specific sparse components, then uses an attention-weighted soft prompt to guide an LLM for sentiment, emotion, and h...
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