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Does Fake News in Different Languages Tell the Same Story? An Analysis of Multi-level Thematic and Emotional Characteristics of News about COVID-19

Author

Listed:
  • Lina Zhou

    (University of North Carolina at Charlotte)

  • Jie Tao

    (Fairfield University)

  • Dongsong Zhang

    (University of North Carolina at Charlotte)

Abstract

Fake news is being generated in different languages, yet existing studies are dominated by English news. The analysis of fake news content has focused on lexical and stylometric features, giving little attention to semantic features. A few studies involving semantic features have either used them as the inputs to classifiers with no interpretations, or treated them in isolation. This research aims to investigate both thematic and emotional characteristics of fake news at different levels and compare them between different languages for the first time. It extends a state-of-the-art topic modeling technique to extract news topics and introduces a divergence measure to assess the importance of thematic characteristics for identifying fake news. We further examine associations of the thematic and emotional characteristics of fake news. The empirical findings have implications for developing both general and language-specific countermeasures for fake news.

Suggested Citation

  • Lina Zhou & Jie Tao & Dongsong Zhang, 2023. "Does Fake News in Different Languages Tell the Same Story? An Analysis of Multi-level Thematic and Emotional Characteristics of News about COVID-19," Information Systems Frontiers, Springer, vol. 25(2), pages 493-512, April.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:2:d:10.1007_s10796-022-10329-7
    DOI: 10.1007/s10796-022-10329-7
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    References listed on IDEAS

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    1. Gupta, Ashish & Li, Han & Farnoush, Alireza & Jiang, Wenting, 2022. "Understanding patterns of COVID infodemic: A systematic and pragmatic approach to curb fake news," Journal of Business Research, Elsevier, vol. 140(C), pages 670-683.
    2. Lina Zhou & Judee K. Burgoon & Jay F. Nunamaker & Doug Twitchell, 2004. "Automating Linguistics-Based Cues for Detecting Deception in Text-Based Asynchronous Computer-Mediated Communications," Group Decision and Negotiation, Springer, vol. 13(1), pages 81-106, January.
    3. Edson C. Tandoc Jr. & Ryan J. Thomas & Lauren Bishop, 2021. "What Is (Fake) News? Analyzing News Values (and More) in Fake Stories," Media and Communication, Cogitatio Press, vol. 9(1), pages 110-119.
    4. Hugo Queiroz Abonizio & Janaina Ignacio de Morais & Gabriel Marques Tavares & Sylvio Barbon Junior, 2020. "Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features," Future Internet, MDPI, vol. 12(5), pages 1-18, May.
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    Cited by:

    1. Sagar Samtani & Ziming Zhao & Ram Krishnan, 2023. "Secure Knowledge Management and Cybersecurity in the Era of Artificial Intelligence," Information Systems Frontiers, Springer, vol. 25(2), pages 425-429, April.

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