This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2607.27393\">AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.06196\">Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.23581\">Verification-Notebook Learning for Source-Aware Multimodal Misinformation Detection</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.19011\">Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.17182\">BanClickThumb: A Multimodal Dataset and Transformer Fusion Benchmarks for Clickbait Detection in Bengali YouTube Videos</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.26465\">MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2608.17895\">BEAR-Bench: A Bilingual Enterprise and Academic Reasoning Benchmark for Multimodal Models</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code>@librarian-bot recommend</code></p>\n","updatedAt":"2026-09-02T01:26:48.364Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":379,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7079522013664246},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.29681","authors":[{"_id":"6a96e65d43140794820a77cd","name":"Nadav Borenstein","hidden":false},{"_id":"6a96e65d43140794820a77ce","user":{"_id":"6698cffdb2ebada9f4a7e7d7","avatarUrl":"/avatars/e66d946c14595d3b008185f2be8d2f57.svg","isPro":false,"fullname":"Greta Warren","user":"gretawarren","type":"user","name":"gretawarren"},"name":"Greta Warren","status":"claimed_verified","statusLastChangedAt":"2026-09-01T16:45:04.998Z","hidden":false},{"_id":"6a96e65d43140794820a77cf","name":"Desmond Elliott","hidden":false},{"_id":"6a96e65d43140794820a77d0","name":"Isabelle Augenstein","hidden":false}],"publishedAt":"2026-08-30T00:00:00.000Z","submittedOnDailyAt":"2026-09-01T00:00:00.000Z","title":"MMMMM: A Unified Taxonomy for Investigating the Mechanisms of Multilingual MultiModal Misinformation","submittedOnDailyBy":{"_id":"6698cffdb2ebada9f4a7e7d7","avatarUrl":"/avatars/e66d946c14595d3b008185f2be8d2f57.svg","isPro":false,"fullname":"Greta Warren","user":"gretawarren","type":"user","name":"gretawarren"},"summary":"Multimodal misinformation on social media is highly prevalent, potent, and harmful, yet difficult to detect and counter, and still poorly understood compared to its text-only counterpart. Research on the properties and deceptive strategies of multimodal misinformation is hindered by a lack of taxonomies grounded in real-world contexts and by the limitations of current multimodal machine learning models, which prevent the automation of annotation and analysis at scale. We address these shortcomings in three steps. First, we collect a large-scale, high-quality dataset of real-world misinformation instances from Twitter/X in seven languages. Second, we develop a novel, comprehensive taxonomy of multimodal misinformation grounded in an in-depth qualitative analysis of the data and prior theoretical work. Finally, we operationalise the taxonomy through an automated multi-step annotation pipeline using a Vision-Language Model (VLM), and perform human-validation. 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MMMMM: A Unified Taxonomy for Investigating the Mechanisms of Multilingual MultiModal Misinformation
Abstract
Multimodal misinformation on social media is highly prevalent, potent, and harmful, yet difficult to detect and counter, and still poorly understood compared to its text-only counterpart. Research on the properties and deceptive strategies of multimodal misinformation is hindered by a lack of taxonomies grounded in real-world contexts and by the limitations of current multimodal machine learning models, which prevent the automation of annotation and analysis at scale. We address these shortcomings in three steps. First, we collect a large-scale, high-quality dataset of real-world misinformation instances from Twitter/X in seven languages. Second, we develop a novel, comprehensive taxonomy of multimodal misinformation grounded in an in-depth qualitative analysis of the data and prior theoretical work. Finally, we operationalise the taxonomy through an automated multi-step annotation pipeline using a Vision-Language Model (VLM), and perform human-validation. Our novel approach leads to previously undocumented insights about how social media users combine images with text to spread misinformation in the wild, e.g., that AI-generated content is particularly prevalent in technology and science, while vaccination misinformation disproportionately utilises images from news outlets to assert credibility. Our method and findings provide guidance for targeted approaches for detecting multimodal misinformation, and suggest that mitigation efforts should be developed and applied strategically rather than uniformly.
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