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Detecting fake news stories via multimodal analysis

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  • Vivek K. Singh
  • Isha Ghosh
  • Darshan Sonagara

Abstract

Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine‐learning techniques. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection.

Suggested Citation

  • Vivek K. Singh & Isha Ghosh & Darshan Sonagara, 2021. "Detecting fake news stories via multimodal analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 3-17, January.
  • Handle: RePEc:bla:jinfst:v:72:y:2021:i:1:p:3-17
    DOI: 10.1002/asi.24359
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    References listed on IDEAS

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    1. Alessandro Bessi & Mauro Coletto & George Alexandru Davidescu & Antonio Scala & Guido Caldarelli & Walter Quattrociocchi, 2015. "Science vs Conspiracy: Collective Narratives in the Age of Misinformation," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
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    Cited by:

    1. Hamby, Anne & Kim, Hongmin & Spezzano, Francesca, 2024. "Sensational stories: The role of narrative characteristics in distinguishing real and fake news and predicting their spread," Journal of Business Research, Elsevier, vol. 170(C).
    2. Nitin Verma & Kenneth R. Fleischmann & Le Zhou & Bo Xie & Min Kyung Lee & Kate Rich & Kristina Shiroma & Chenyan Jia & Tara Zimmerman, 2022. "Trust in COVID‐19 public health information," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1776-1792, December.
    3. Isha Ghosh & Vivek Singh, 2022. "“Not all my friends are friends”: Audience‐group‐based nudges for managing location privacy," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 797-810, June.
    4. Dr. Diala Edwin Lionel & Dr. Uzowuihe Bertha, 2024. "Visual Elements in Digital Technology Platforms and Visualized Communication in Spreading of False Information," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(3s), pages 2118-2138, March.

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