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DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation

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arxiv 2205.12617 v1 pith:4HRL2CAT submitted 2022-05-25 cs.CL cs.AIcs.CV

DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation

Jingnong Qu , Liunian Harold Li , Jieyu Zhao , Sunipa Dev , Kai-Wei Chang This is my paper
classification cs.CL cs.AIcs.CV
keywords datasetdisinformationmemesmultimodalcurrentdisinfomememodelsmultiple
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Disinformation has become a serious problem on social media. In particular, given their short format, visual attraction, and humorous nature, memes have a significant advantage in dissemination among online communities, making them an effective vehicle for the spread of disinformation. We present DisinfoMeme to help detect disinformation memes. The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veganism/vegetarianism. The dataset poses multiple unique challenges: limited data and label imbalance, reliance on external knowledge, multimodal reasoning, layout dependency, and noise from OCR. We test multiple widely-used unimodal and multimodal models on this dataset. The experiments show that the room for improvement is still huge for current models.

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Cited by 2 Pith papers

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

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

    cs.CL 2024-06 accept novelty 6.0

    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.

  2. DARC-CLIP: Dynamic Adaptive Refinement with Cross-Attention for Meme Understanding

    cs.CL 2026-04 unverdicted novelty 4.0

    DARC-CLIP improves CLIP-based meme classification with hierarchical adaptive refinement, delivering +4.18 AUROC and +6.84 F1 gains in hate detection on PrideMM and CrisisHateMM benchmarks.