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A Survey on Multimodal Disinformation Detection

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arxiv 2103.12541 v2 pith:OBFQLOOK submitted 2021-03-13 cs.MM cs.AIcs.CLcs.CRcs.CYcs.LGcs.SI

classification cs.MMcs.AIcs.CLcs.CRcs.CYcs.LGcs.SI
keywords disinformationcontentdetectionfactualitymodalitiesmultimodalwhilecombinations
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused on factuality, others investigated how harmful the content is. While these two components in the definition of disinformation (i) factuality, and (ii) harmfulness, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 13 citations worldwide. Full citation record

  1. Echoes of Unrest: A Multimodal NLP Framework for Early Warning of Fake News and Violence-Driven Mob Activity

    cs.CL 2026-07 conditional novelty 4.5 of 10

    An attention-fused XLM-RoBERTa/CLIP model with sarcasm and geo metadata reaches 98% accuracy on a heterogeneous 138k Bangla-English fake-news corpus and supports hotspot maps.

  2. ISMAF: Intrinsic-Social Modality Alignment and Fusion for Multimodal Rumor Detection

    cs.MM 2025-05 conditional novelty 4.0 of 10

    ISMAF reports state-of-the-art rumor detection accuracy on Weibo and PHEME by aligning text-image intrinsic features with social graph features and fusing them adaptively.

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