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Prompted weak supervision boosts meme hate speech detection across languages

Researchers have developed a prompted weak supervision (PWS) method to improve hate speech detection in memes, addressing the challenges posed by their multimodal nature and subtle cultural cues. This approach breaks down meme understanding into targeted, question-based labeling functions, outperforming direct classification by vision-language models. The PWS method showed significant gains in multilingual contexts, particularly for Chinese and Hindi, achieving top rankings in the LT-EDI 2026 shared task. AI

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IMPACT Introduces a novel approach for multimodal hate speech detection, potentially improving safety measures in online content moderation.

RANK_REASON The cluster contains an academic paper detailing a new methodology for a specific AI task.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Ivo Bueno, Lea Hirlimann, Enkelejda Kasneci ·

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

    arXiv:2604.24179v1 Announce Type: new Abstract: Detecting hate speech in memes is challenging due to their multimodal nature and subtle, culturally grounded cues such as sarcasm and context. While recent vision-language models (VLMs) enable joint reasoning over text and images, e…

  2. arXiv cs.CL TIER_1 · Enkelejda Kasneci ·

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

    Detecting hate speech in memes is challenging due to their multimodal nature and subtle, culturally grounded cues such as sarcasm and context. While recent vision-language models (VLMs) enable joint reasoning over text and images, end-to-end prompting can be brittle, as a single …