MMMMM: A Unified Taxonomy for Investigating the Mechanisms of Multilingual MultiModal Misinformation

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multimodal-misinformation taxonomy multilingual social-media vlm dataset

Summary

This paper presents a unified taxonomy for investigating multilingual multimodal misinformation on social media, using a large-scale dataset and automated annotation with a Vision-Language Model to uncover insights for detection and mitigation.

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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Source: https://huggingface.co/papers/2608.29681

Abstract

Multimodalmisinformationonsocialmediaishighlyprevalent,potent,andharmful,yetdifficulttodetectandcounter,andstillpoorlyunderstoodcomparedtoitstext-onlycounterpart.Researchonthepropertiesanddeceptivestrategiesofmultimodalmisinformationishinderedbyalackoftaxonomiesgroundedinreal-worldcontextsandbythelimitationsofcurrentmultimodalmachinelearningmodels,whichpreventtheautomationofannotationandanalysisatscale.Weaddresstheseshortcomingsinthreesteps.First,wecollectalarge-scale,high-qualitydatasetofreal-worldmisinformationinstancesfromTwitter/Xinsevenlanguages.Second,wedevelopanovel,comprehensivetaxonomyofmultimodalmisinformationgroundedinanin-depthqualitativeanalysisofthedataandpriortheoreticalwork.Finally,weoperationalisethetaxonomythroughanautomatedmulti-stepannotationpipelineusingaVision-LanguageModel(VLM),andperformhuman-validation.Ournovelapproachleadstopreviouslyundocumentedinsightsabouthowsocialmediauserscombineimageswithtexttospreadmisinformationinthewild,e.g.,thatAI-generatedcontentisparticularlyprevalentintechnologyandscience,whilevaccinationmisinformationdisproportionatelyutilisesimagesfromnewsoutletstoassertcredibility.Ourmethodandfindingsprovideguidancefortargetedapproachesfordetectingmultimodalmisinformation,andsuggestthatmitigationeffortsshouldbedevelopedandappliedstrategicallyratherthanuniformly.

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