Understanding the Use of Fauxtography on Social Media
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Abstract (arXiv)
Despite the influence that image-based communication has on online discourse, the role played by images in disinformation is still not well understood. In this paper, we present the first large-scale study of fauxtography, analyzing the use of manipulated or misleading images in news discussion on online communities. First, we develop a computational pipeline geared to detect fauxtography, and identify over 61k instances of fauxtography discussed on Twitter, 4chan, and Reddit. Then, we study how posting fauxtography affects engagement of posts on social media, finding that posts containing it receive more interactions in the form of re-shares, likes, and comments. Finally, we show that fauxtography images are often turned into memes by Web communities. Our findings show that effective mitigation against disinformation need to take images into account, and highlight a number of challenges in dealing with image-based disinformation.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 2.
9 papers.
- 2021Detecting Harmful Memes and Their Targetscited by 77
- 2021Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resourcescited by 71
- 2021A Survey on Multimodal Disinformation Detectioncited by 47
- 2024DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Expertscited by 42
- 2023Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformationcited by 21
- 2022DISARM: Detecting the Victims Targeted by Harmful Memescited by 20
- 2026VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checkingcited by 5
- 2024PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twittercited by 0
- 2026XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deceptioncited by 0
- 2026XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception23 Mar 2026
- 2026VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking13 Jan 2026
- 2024DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts13 Dec 2024
- 2024PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter30 Jul 2024
- 2023Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation3 Nov 2023
- 2022DISARM: Detecting the Victims Targeted by Harmful Memes11 May 2022
- 2021Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources30 Nov 2021
- 2021Detecting Harmful Memes and Their Targets24 Sep 2021
- 2021A Survey on Multimodal Disinformation Detection13 Mar 2021