IDEAS home Printed from https://ideas.repec.org/a/sae/jinter/v36y2024i1p58-78.html
   My bibliography  Save this article

Determinants of Gullibility to Misinformation: A Study of Climate Change, COVID-19 and Artificial Intelligence

Author

Listed:
  • Sven Gruener

Abstract

This article explores whether susceptibility to misinformation is context-dependent. For this purpose, a survey experiment has been conducted in which subjects from Germany had to rate the reliability of several statements in the fields of climate change, COVID-19 and artificial intelligence. These contexts differed with respect to the frequency of media coverage, population activity in the form of demonstrations, daily number of deaths, and scientific knowledge. We find some similarities (for example, trust in social networks is positively associated with falling for misinformation in all three contexts) but also substantial differences (for example, risk perception as well as the extent to which people consider evidence to adjust their beliefs seem to matter for climate change and COVID-19 but not for artificial intelligence). More systematic work on context-related differences and narratives is required to design adequate measures against misinformation. JEL: C91, D01, D80

Suggested Citation

  • Sven Gruener, 2024. "Determinants of Gullibility to Misinformation: A Study of Climate Change, COVID-19 and Artificial Intelligence," Journal of Interdisciplinary Economics, , vol. 36(1), pages 58-78, January.
  • Handle: RePEc:sae:jinter:v:36:y:2024:i:1:p:58-78
    DOI: 10.1177/02601079221083482
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/02601079221083482
    Download Restriction: no

    File URL: https://libkey.io/10.1177/02601079221083482?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    2. Pietro Ortoleva & Erik Snowberg, 2015. "Overconfidence in Political Behavior," American Economic Review, American Economic Association, vol. 105(2), pages 504-535, February.
    3. repec:cup:judgdm:v:11:y:2016:i:1:p:99-113 is not listed on IDEAS
    4. Gerd Gigerenzer & Ralph Hertwig & Eva Van Den Broek & Barbara Fasolo & Konstantinos V. Katsikopoulos, 2005. "“A 30% Chance of Rain Tomorrow”: How Does the Public Understand Probabilistic Weather Forecasts?," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 623-629, June.
    5. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    6. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
    7. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
    8. Flynn, Leisa Reinecke & Goldsmith, Ronald E., 1999. "A Short, Reliable Measure of Subjective Knowledge," Journal of Business Research, Elsevier, vol. 46(1), pages 57-66, September.
    9. Russell Golman & David Hagmann & George Loewenstein, 2017. "Information Avoidance," Journal of Economic Literature, American Economic Association, vol. 55(1), pages 96-135, March.
    10. repec:cup:judgdm:v:7:y:2012:i:1:p:25-47 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gruener, Sven, 2021. "Susceptibility to misinformation: a study of climate change, Covid-19, and artificial intelligence," SocArXiv x8efq, Center for Open Science.
    2. Gruener, Sven, 2021. "Misinformation: determinants of gullibility," SocArXiv r3fx7, Center for Open Science.
    3. Lohse, Johannes & McDonald, Rebecca, 2021. "Absolute groupishness and the demand for information," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242454, Verein für Socialpolitik / German Economic Association.
    4. Felix Chopra & Ingar K. Haaland & Christopher Roth, 2019. "Do People Value More Informative News?," CESifo Working Paper Series 8026, CESifo.
    5. David L. Dickinson, 2020. "Deliberation enhances the confirmation bias. An examination of politics and religion," Working Papers 20-06, Department of Economics, Appalachian State University.
    6. Buser, Thomas, 2024. "Adversarial Economic Preferences Predict Right-Wing Voting," IZA Discussion Papers 16711, Institute of Labor Economics (IZA).
    7. Assenza, Tiziana & Cardaci, Alberto & Huber, Stefanie, 2024. "Fake News: Susceptibility, Awareness and Solutions," TSE Working Papers 24-1519, Toulouse School of Economics (TSE), revised Apr 2024.
    8. James Flamino & Alessandro Galeazzi & Stuart Feldman & Michael W. Macy & Brendan Cross & Zhenkun Zhou & Matteo Serafino & Alexandre Bovet & Hernán A. Makse & Boleslaw K. Szymanski, 2023. "Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections," Nature Human Behaviour, Nature, vol. 7(6), pages 904-916, June.
    9. Matthew Spradling & Jeremy Straub, 2022. "Evaluation of the Factors That Impact the Perception of Online Content Trustworthiness by Income, Political Affiliation and Online Usage Time," Future Internet, MDPI, vol. 14(11), pages 1-55, November.
    10. Dickinson, David L., 2024. "Deliberation, mood response, and the confirmation bias in the religious belief domain," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 109(C).
    11. Lodh, Rishab & Dey, Oindrila, 2023. "“Fake news alert!”: A game of misinformation and news consumption behavior," MPRA Paper 118371, University Library of Munich, Germany.
    12. Yevgeniy Golovchenko, 2020. "Measuring the scope of pro-Kremlin disinformation on Twitter," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-11, December.
    13. Jeremy Straub & Matthew Spradling & Bob Fedor, 2022. "Assessment of Factors Impacting the Perception of Online Content Trustworthiness by Age, Education and Gender," Societies, MDPI, vol. 12(2), pages 1-66, March.
    14. Bräuninger, Thomas & Marinov, Nikolay, 2022. "Political elites and the “War on Truth’’," Journal of Public Economics, Elsevier, vol. 206(C).
    15. Kathie M. d'I. Treen & Hywel T. P. Williams & Saffron J. O'Neill, 2020. "Online misinformation about climate change," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(5), September.
    16. Assenza, Tiziana, 2021. "The Ability to 'Distill the Truth'," TSE Working Papers 21-1280, Toulouse School of Economics (TSE), revised Mar 2022.
    17. Marius Dragomir & José Rúas-Araújo & Minna Horowitz, 2024. "Beyond online disinformation: assessing national information resilience in four European countries," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    18. Gruener, Sven, 2020. "Identifying and debunking environmental-related false news stories—An experimental study," SocArXiv zmx5p, Center for Open Science.
    19. Momsen, Katharina & Ohndorf, Markus, 2020. "Information Avoidance, Selective Exposure, and Fake(?) News - A Market Experiment," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224637, Verein für Socialpolitik / German Economic Association.
    20. Grunewald, Andreas & Klockmann, Victor & von Schenk, Alicia & von Siemens, Ferdinand, 2024. "Are biases contagious? The influence of communication on motivated beliefs," W.E.P. - Würzburg Economic Papers 109, University of Würzburg, Department of Economics.

    More about this item

    Keywords

    False news stories; monological belief system; COVID-19; climate change; artificial intelligence;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jinter:v:36:y:2024:i:1:p:58-78. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.