IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v67y2021ics0160791x21002633.html
   My bibliography  Save this article

Verifying online information: Development and validation of a self-report scale

Author

Listed:
  • Tifferet, Sigal

Abstract

Misinformation endangers democracy, science, and rational behavior. Verifying information and recognizing misinformation are critical skills, but there are few measures of these abilities. To help close this gap, we developed and validated the Verifying Online Information (VOI) self-report scale, which assesses individual differences in online information verification. Two study samples were collected through Amazon Mechanical Turk (N = 958). In Study 1, exploratory factor analysis suggested a 22-item scale (VOI-22; α = 0.95) with two underlying factors: direct and indirect verification of online information. In Study 2, the bifactor model was affirmed using confirmatory factor analysis. Convergent validity was demonstrated with the positive factor Need for Cognition, and discriminant validity was demonstrated with social desirability. Two abbreviated scales (with three and seven items) were also created and validated using genetic algorithms. VOI will allow researchers and educators to evaluate behaviors associated with verifying online information, making it a critical tool in the fight against misinformation.

Suggested Citation

  • Tifferet, Sigal, 2021. "Verifying online information: Development and validation of a self-report scale," Technology in Society, Elsevier, vol. 67(C).
  • Handle: RePEc:eee:teinso:v:67:y:2021:i:c:s0160791x21002633
    DOI: 10.1016/j.techsoc.2021.101788
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X21002633
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techsoc.2021.101788?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Apuke, Oberiri Destiny & Omar, Bahiyah, 2021. "The ethical challenges and issues of online journalism practice in Nigeria: What do professionals and academics think?," Technology in Society, Elsevier, vol. 67(C).
    3. Xijuan Zhang & Ramsha Noor & Victoria Savalei, 2016. "Examining the Effect of Reverse Worded Items on the Factor Structure of the Need for Cognition Scale," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
    4. repec:cup:judgdm:v:5:y:2010:i:5:p:411-419 is not listed on IDEAS
    5. Dietram A. Scheufele & Nicole M. Krause, 2019. "Science audiences, misinformation, and fake news," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(16), pages 7662-7669, April.
    6. Berinsky, Adam J., 2017. "Rumors and Health Care Reform: Experiments in Political Misinformation," British Journal of Political Science, Cambridge University Press, vol. 47(2), pages 241-262, April.
    7. repec:cup:judgdm:v:10:y:2015:i:6:p:549-563 is not listed on IDEAS
    8. Ulrich Schroeders & Oliver Wilhelm & Gabriel Olaru, 2016. "Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-19, November.
    9. Matthew J C Crump & John V McDonnell & Todd M Gureckis, 2013. "Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-18, March.
    10. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    11. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
    12. 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.
    13. Ana I. Bento & Thuy Nguyen & Coady Wing & Felipe Lozano-Rojas & Yong-Yeol Ahn & Kosali Simon, 2020. "Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(21), pages 11220-11222, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Yunjian & Song, Yixiao & Sun, Yanming & Zeng, Mingzhuo, 2024. "When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity," Technology in Society, Elsevier, vol. 77(C).
    2. Raj, Chahat & Meel, Priyanka, 2022. "People lie, actions Don't! Modeling infodemic proliferation predictors among social media users," Technology in Society, Elsevier, vol. 68(C).
    3. Shixiong Wang & Fangfang Su & Lu Ye & Yuan Jing, 2022. "Disinformation: A Bibliometric Review," IJERPH, MDPI, vol. 19(24), pages 1-21, December.
    4. Dong, Qingxing & Xiong, Siyue & Zhang, Mengyi, 2024. "Remedial behavior for misinformation: A moderated mediation model of remedial attitude and perceived consequence severity," Technology in Society, Elsevier, vol. 77(C).

    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. Buser, Thomas, 2024. "Adversarial Economic Preferences Predict Right-Wing Voting," IZA Discussion Papers 16711, Institute of Labor Economics (IZA).
    2. Assenza, Tiziana & Cardaci, Alberto & Huber, Stefanie, 2024. "Fake News: Susceptibility, Awareness and Solutions," TSE Working Papers 24-1519, Toulouse School of Economics (TSE), revised Nov 2024.
    3. Vicente Javier Clemente-Suárez & Eduardo Navarro-Jiménez & Juan Antonio Simón-Sanjurjo & Ana Isabel Beltran-Velasco & Carmen Cecilia Laborde-Cárdenas & Juan Camilo Benitez-Agudelo & Álvaro Bustamante-, 2022. "Mis–Dis Information in COVID-19 Health Crisis: A Narrative Review," IJERPH, MDPI, vol. 19(9), pages 1-24, April.
    4. Matilde Giaccherini & Joanna Kopinska & Gabriele Rovigatti, 2022. "Vax Populi: The Social Costs of Online Vaccine Skepticism," CESifo Working Paper Series 10184, CESifo.
    5. Jost, Peter J. & Pünder, Johanna & Schulze-Lohoff, Isabell, 2020. "Fake news - Does perception matter more than the truth?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 85(C).
    6. Fabio Padovano & Pauline Mille, 2022. "Education, fake news and the PBC," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2022-01-ccr, Condorcet Center for political Economy.
    7. Jay J. Van Bavel & Katherine Baicker & Paulo S. Boggio & Valerio Capraro & Aleksandra Cichocka & Mina Cikara & Molly J. Crockett & Alia J. Crum & Karen M. Douglas & James N. Druckman & John Drury & Oe, 2020. "Using social and behavioural science to support COVID-19 pandemic response," Nature Human Behaviour, Nature, vol. 4(5), pages 460-471, May.
    8. Assenza, Tiziana, 2021. "The Ability to 'Distill the Truth'," TSE Working Papers 21-1280, Toulouse School of Economics (TSE), revised Mar 2022.
    9. 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.
    10. Jiexun Li & Xiaohui Chang, 2023. "Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media," Information Systems Frontiers, Springer, vol. 25(4), pages 1479-1493, August.
    11. Gruener, Sven, 2020. "Identifying and debunking environmental-related false news stories—An experimental study," SocArXiv zmx5p, Center for Open Science.
    12. Armand, Alex & Augsburg, Britta & Bancalari, Antonella & Kameshwara, Kalyan Kumar, 2024. "Religious proximity and misinformation: Experimental evidence from a mobile phone-based campaign in India," Journal of Health Economics, Elsevier, vol. 96(C).
    13. Fabio Padovano & Pauline Mille, 2023. "Education, fake news and the Political Budget Cycle," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2023-01-ccr, Condorcet Center for political Economy.
    14. Xiangyu Wang & Min Zhang & Weiguo Fan & Kang Zhao, 2022. "Understanding the spread of COVID‐19 misinformation on social media: The effects of topics and a political leader's nudge," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 726-737, May.
    15. Leopoldo Fergusson & Carlos Molina, 2020. "Facebook Causes Protests," HiCN Working Papers 323, Households in Conflict Network.
    16. Dean Neu & Gregory D. Saxton & Abu S. Rahaman, 2022. "Social Accountability, Ethics, and the Occupy Wall Street Protests," Journal of Business Ethics, Springer, vol. 180(1), pages 17-31, September.
    17. Robbett, Andrea & Matthews, Peter Hans, 2018. "Partisan bias and expressive voting," Journal of Public Economics, Elsevier, vol. 157(C), pages 107-120.
    18. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    19. Fathey Mohammed & Nabil Hasan Al-Kumaim & Ahmed Ibrahim Alzahrani & Yousef Fazea, 2023. "The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    20. Bartosz Wilczek, 2020. "Misinformation and herd behavior in media markets: A cross-national investigation of how tabloids’ attention to misinformation drives broadsheets’ attention to misinformation in political and business," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.

    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:eee:teinso:v:67:y:2021:i:c:s0160791x21002633. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    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.