Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities
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Abstract (arXiv)
As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Taking advantage of the fact that visual modalities such as images and videos are more favorable and attractive to the users and textual contents are sometimes skimmed carelessly, misinformation spreaders have recently targeted contextual connections between the modalities e.g., text and image. Hence many researchers have developed automatic techniques for detecting possible cross-modal discordance in web-based content. We analyze, categorize and identify existing approaches in addition to challenges and shortcomings they face in order to unearth new research opportunities in the field of multi-modal misinformation detection.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 49 of its 123 references that are in the map. First posted on arXiv 25 Mar 2022.
Builds on
49 of 123 references.
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37 more
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Citing papers per year: 2023: 3; 2024: 3; 2025: 6; 2026: 2.
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- 2023Multimodal Automated Fact-Checking: A Surveycited by 51
- 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checkingcited by 34
- 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platformcited by 19
- 2023Combating Online Misinformation Videos: Characterization, Detection, and Future Directionscited by 14
- 2024A Guide to Misinformation Detection Data and Evaluationcited by 10
- 2026The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detectioncited by 6
- 2025RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checkingcited by 5
- 2024Fake News Detection: It's All in the Data!cited by 4
- 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucinationcited by 2
- 2025Effectiveness of Large Multimodal Models in Detecting Disinformation: Experimental Resultscited by 1
- 2024Dynamic Analysis and Adaptive Discriminator for Fake News Detectioncited by 0
- 2025Towards Explainable Bilingual Multimodal Misinformation Detection and Localizationcited by 0
- 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior17 Aug 2026
- 2026The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection16 Jan 2026
- 2025Effectiveness of Large Multimodal Models in Detecting Disinformation: Experimental Results26 Sep 2025
- 2025MM-FusionNet: Context-Aware Dynamic Fusion for Multi-modal Fake News Detection with Large Vision-Language Models5 Aug 2025
- 2025RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking12 Jul 2025
- 2025Towards Explainable Bilingual Multimodal Misinformation Detection and Localization28 Jun 2025
- 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform21 Apr 2025
- 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination16 Apr 2025
- 2024A Guide to Misinformation Detection Data and Evaluation7 Nov 2024
- 2024Dynamic Analysis and Adaptive Discriminator for Fake News Detection20 Aug 2024
- 2024Fake News Detection: It's All in the Data!2 Jul 2024
- 2023Multimodal Automated Fact-Checking: A Survey22 May 2023
- 2023Multimodal Automated Fact-Checking: A Surveycited by 51
- 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checkingcited by 34
- 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platformcited by 19
- 2023Combating Online Misinformation Videos: Characterization, Detection, and Future Directionscited by 14
- 2024A Guide to Misinformation Detection Data and Evaluationcited by 10
- 2024Fake News Detection: It's All in the Data!cited by 4
- 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucinationcited by 2
- 2025Effectiveness of Large Multimodal Models in Detecting Disinformation: Experimental Resultscited by 1
- 2024Dynamic Analysis and Adaptive Discriminator for Fake News Detectioncited by 0
- 2025Towards Explainable Bilingual Multimodal Misinformation Detection and Localizationcited by 0
- 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behaviorcited by 0
- 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior17 Aug 2026
- 2025Effectiveness of Large Multimodal Models in Detecting Disinformation: Experimental Results26 Sep 2025
- 2025Towards Explainable Bilingual Multimodal Misinformation Detection and Localization28 Jun 2025
- 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform21 Apr 2025
- 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination16 Apr 2025
- 2024A Guide to Misinformation Detection Data and Evaluation7 Nov 2024
- 2024Dynamic Analysis and Adaptive Discriminator for Fake News Detection20 Aug 2024
- 2024Fake News Detection: It's All in the Data!2 Jul 2024
- 2023Multimodal Automated Fact-Checking: A Survey22 May 2023
- 2023Combating Online Misinformation Videos: Characterization, Detection, and Future Directions7 Feb 2023
- 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking27 Jan 2023