IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v26y2024i2d10.1007_s10796-022-10347-5.html
   My bibliography  Save this article

Digital Resilience in Dealing with Misinformation on Social Media during COVID-19

Author

Listed:
  • Stefka Schmid

    (TU Darmstadt, Science and Technology for Peace and Security (PEASEC))

  • Katrin Hartwig

    (TU Darmstadt, Science and Technology for Peace and Security (PEASEC))

  • Robert Cieslinski

    (TU Darmstadt, Science and Technology for Peace and Security (PEASEC))

  • Christian Reuter

    (TU Darmstadt, Science and Technology for Peace and Security (PEASEC))

Abstract

In crises such as the COVID-19 pandemic, it is crucial to support users when dealing with social media content. Considering digital resilience, we propose a web app based on Social Network Analysis (SNA) to provide an overview of potentially misleading vs. non-misleading content on Twitter, which can be explored by users and enable foundational learning. The latter aims at systematically identifying thematic patterns which may be associated with misleading information. Additionally, it entails reflecting on indicators of misleading tweets which are proposed to approach classification of tweets. Paying special attention to non-expert users of social media, we conducted a two-step Think Aloud study for evaluation. While participants valued the opportunity to generate new knowledge and the diversity of the application, qualities such as equality and rapidity may be further improved. However, learning effects outweighed individual costs as all users were able to shift focus onto relevant features, such as hashtags, while readily pointing out content characteristics. Our design artifact connects to learning-oriented interventions regarding the spread of misleading information and tackles information overload by a SNA-based plug-in.

Suggested Citation

  • Stefka Schmid & Katrin Hartwig & Robert Cieslinski & Christian Reuter, 2024. "Digital Resilience in Dealing with Misinformation on Social Media during COVID-19," Information Systems Frontiers, Springer, vol. 26(2), pages 477-499, April.
  • Handle: RePEc:spr:infosf:v:26:y:2024:i:2:d:10.1007_s10796-022-10347-5
    DOI: 10.1007/s10796-022-10347-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-022-10347-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10796-022-10347-5?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Marc-André Kaufhold & Nicola Rupp & Christian Reuter & Matthias Habdank, 2020. "Mitigating information overload in social media during conflicts and crises: design and evaluation of a cross-platform alerting system," Behaviour and Information Technology, Taylor & Francis Journals, vol. 39(3), pages 319-342, March.
    2. Nicole M. Krause & Isabelle Freiling & Becca Beets & Dominique Brossard, 2020. "Fact-checking as risk communication: the multi-layered risk of misinformation in times of COVID-19," Journal of Risk Research, Taylor & Francis Journals, vol. 23(7-8), pages 1052-1059, August.
    3. Chengcheng Shao & Pik-Mai Hui & Lei Wang & Xinwen Jiang & Alessandro Flammini & Filippo Menczer & Giovanni Luca Ciampaglia, 2018. "Anatomy of an online misinformation network," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    4. Elena Milani & Emma Weitkamp & Peter Webb, 2020. "The Visual Vaccine Debate on Twitter: A Social Network Analysis," Media and Communication, Cogitatio Press, vol. 8(2), pages 364-375.
    5. Hyehyun Hong & Hyo Jung Kim, 2020. "Antecedents and Consequences of Information Overload in the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(24), pages 1-15, December.
    6. Gordon Pennycook & Ziv Epstein & Mohsen Mosleh & Antonio A. Arechar & Dean Eckles & David G. Rand, 2021. "Shifting attention to accuracy can reduce misinformation online," Nature, Nature, vol. 592(7855), pages 590-595, April.
    7. Elena Milani & Emma Weitkamp & Peter Webb, 2020. "The Visual Vaccine Debate on Twitter: A Social Network Analysis," Media and Communication, Cogitatio Press, vol. 8(2), pages 364-375.
    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. Zhao, Xiaoquan & Horoszko, Urszula A. & Murphy, Amy & Taylor, Bruce G. & Lamuda, Phoebe A. & Pollack, Harold A. & Schneider, John A. & Taxman, Faye S., 2023. "Openness to change among COVID misinformation endorsers: Associations with social demographic characteristics and information source usage," Social Science & Medicine, Elsevier, vol. 335(C).
    2. Mack, Philipp & Wallin, Ida & Zwickel, Mariella Susann & Pfistner, Jonas & König, Lena & Kleinschmit, Daniela, 2025. "Calling into the void? German forest dieback 2.0 debate on Twitter. A case study to operationalize the analysis of discursive power in hybrid media systems," Forest Policy and Economics, Elsevier, vol. 172(C).
    3. 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.
    4. Nicolás Ajzenman & Bruno Ferman & Sant’Anna Pedro C., 2023. "Discrimination in the Formation of Academic Networks: A Field Experiment on #EconTwitter," Working Papers 235, Red Nacional de Investigadores en Economía (RedNIE).
    5. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    6. Lucia Freira & Marco Sartorio & Cynthia Boruchowicz & Florencia Lopez Boo & Joaquin Navajas, 2021. "The interplay between partisanship, forecasted COVID-19 deaths, and support for preventive policies," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-10, December.
    7. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    8. Margherita, Alessandro & Elia, Gianluca & Klein, Mark, 2021. "Managing the COVID-19 emergency: A coordination framework to enhance response practices and actions," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Xuhao Shao & Ao Li & Chuansheng Chen & Elizabeth F. Loftus & Bi Zhu, 2023. "Cross-stage neural pattern similarity in the hippocampus predicts false memory derived from post-event inaccurate information," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    10. Krishna Dasaratha & Kevin He, 2022. "Learning from Viral Content," Papers 2210.01267, arXiv.org, revised Aug 2023.
    11. Berger, Lara Marie & Kerkhof, Anna & Mindl, Felix & Münster, Johannes, 2025. "Debunking “fake news” on social media: Immediate and short-term effects of fact-checking and media literacy interventions," Journal of Public Economics, Elsevier, vol. 245(C).
    12. Matthew R DeVerna & Rachith Aiyappa & Diogo Pacheco & John Bryden & Filippo Menczer, 2024. "Identifying and characterizing superspreaders of low-credibility content on Twitter," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-17, May.
    13. Melchior, Cristiane & Warin, Thierry & Oliveira, Mirian, 2025. "An investigation of the COVID-19-related fake news sharing on Facebook using a mixed methods approach," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    14. Jing, Fei & Zhang, Zhong & Wu, Jian-Liang & Hu, Die & Zhang, Zi-Ke, 2025. "Quantifying and predicting evolutionary networks," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    15. Grzegorz Drozdowski & Joanna Rogozińska-Mitrut & Jacek Stasiak, 2021. "The Empirical Analysis of the Core Competencies of the Company’s Resource Management Risk. Preliminary Study," Risks, MDPI, vol. 9(6), pages 1-12, June.
    16. Tiziana Assenza & Alberto Cardaci & Stefanie Huber, 2024. "Fake News: Susceptibility, Awareness, and Solutions," ECONtribute Policy Brief Series 065, University of Bonn and University of Cologne, Germany.
    17. Verma, Surabhi & Gustafsson, Anders, 2020. "Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach," Journal of Business Research, Elsevier, vol. 118(C), pages 253-261.
    18. Jack P Hughes & Alexandros Efstratiou & Sara R Komer & Lilli A Baxter & Milica Vasiljevic & Ana C Leite, 2022. "The impact of risk perceptions and belief in conspiracy theories on COVID-19 pandemic-related behaviours," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-20, February.
    19. Andres Karjus & Christine Cuskley, 2024. "Evolving linguistic divergence on polarizing social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    20. repec:hal:journl:hal-03533356 is not listed on IDEAS
    21. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:infosf:v:26:y:2024:i:2:d:10.1007_s10796-022-10347-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.