IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v7y2024i1d10.1007_s42001-024-00253-y.html
   My bibliography  Save this article

An exploratory analysis of COVID bot vs human disinformation dissemination stemming from the Disinformation Dozen on Telegram

Author

Listed:
  • Lynnette Hui Xian Ng

    (Carnegie Mellon University)

  • Ian Kloo

    (Carnegie Mellon University)

  • Samantha Clark

    (Carnegie Mellon University)

  • Kathleen M. Carley

    (Carnegie Mellon University)

Abstract

The COVID-19 pandemic of 2021 led to a worldwide health crisis that was accompanied by an infodemic. A group of 12 social media personalities, dubbed the “Disinformation Dozen”, were identified as key in spreading disinformation regarding the COVID-19 virus, treatments, and vaccines. This study focuses on the spread of disinformation propagated by this group on Telegram, a mobile messaging and social media platform. After segregating users into three groups—the Disinformation Dozen, bots, and humans, we perform an investigation with a dataset of Telegram messages from January to June 2023, comparatively analyzing temporal, topical, and network features. We observe that the Disinformation Dozen are highly involved in the initial dissemination of disinformation but are not the main drivers of the propagation of disinformation. Bot users are extremely active in conversation threads, while human users are active propagators of information, disseminating posts between Telegram channels through the forwarding mechanism.

Suggested Citation

  • Lynnette Hui Xian Ng & Ian Kloo & Samantha Clark & Kathleen M. Carley, 2024. "An exploratory analysis of COVID bot vs human disinformation dissemination stemming from the Disinformation Dozen on Telegram," Journal of Computational Social Science, Springer, vol. 7(1), pages 695-720, April.
  • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00253-y
    DOI: 10.1007/s42001-024-00253-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-024-00253-y
    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/s42001-024-00253-y?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. Ahmed Al-Rawi, 2022. "News loopholing: Telegram news as portable alternative media," Journal of Computational Social Science, Springer, vol. 5(1), pages 949-968, May.
    2. Cantay Caliskan & Alaz Kilicaslan, 2023. "Varieties of corona news: a cross-national study on the foundations of online misinformation production during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 6(1), pages 191-243, April.
    3. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    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. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    2. Howell, Bronwyn E. & Potgieter, Petrus H., 2023. "AI-generated lemons: a sour outlook for content producers?," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277971, International Telecommunications Society (ITS).
    3. Wentao Xu & Kazutoshi Sasahara, 2022. "Characterizing the roles of bots on Twitter during the COVID-19 infodemic," Journal of Computational Social Science, Springer, vol. 5(1), pages 591-609, May.
    4. Vibha Sharma & Fatema Sultana & Sohaib Alam & Sameena Banu, 2024. "Trolling as a Disruptive Tool for Human Rights Violations: An Exploration of the Challenges Faced by Performance Artists," World Journal of English Language, Sciedu Press, vol. 14(4), pages 411-411, July.
    5. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    6. Joshua Uyheng & Kathleen M. Carley, 2020. "Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines," Journal of Computational Social Science, Springer, vol. 3(2), pages 445-468, November.
    7. Junhui Cai & Dan Yang & Wu Zhu & Haipeng Shen & Linda Zhao, 2021. "Network regression and supervised centrality estimation," Papers 2111.12921, arXiv.org.
    8. Yevgeniy Golovchenko, 2020. "Measuring the scope of pro-Kremlin disinformation on Twitter," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-11, December.
    9. Samuel F Rosenblatt & Jeffrey A Smith & G Robin Gauthier & Laurent Hébert-Dufresne, 2020. "Immunization strategies in networks with missing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
    10. 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.
    11. Matilde Giaccherini & Joanna Kopinska & Gabriele Rovigatti, 2022. "Vax Populi: The Social Costs of Online Vaccine Skepticism," CESifo Working Paper Series 10184, CESifo.
    12. Hyehyun Hong & Hyun Jee Oh, 2020. "Utilizing Bots for Sustainable News Business: Understanding Users’ Perspectives of News Bots in the Age of Social Media," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
    13. Wu, Yue & Li, Wenjia & Li, Yixiao & Chen, Qi & Liu, Mingyu & Li, Yuehui, 2024. "Alleviating negative group polarization with the aid of social bots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 644(C).
    14. Min, Yong & Zhou, Yuying & Liu, Yuhang & Zhang, Jian & Xuan, Qi & Jin, Xiaogang & Cai, He, 2021. "The role of degree correlation in shaping filter bubbles in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    15. Andrea Moscadelli & Giuseppe Albora & Massimiliano Alberto Biamonte & Duccio Giorgetti & Michele Innocenzio & Sonia Paoli & Chiara Lorini & Paolo Bonanni & Guglielmo Bonaccorsi, 2020. "Fake News and Covid-19 in Italy: Results of a Quantitative Observational Study," IJERPH, MDPI, vol. 17(16), pages 1-13, August.
    16. Menghan Zhang & Xue Qi & Ze Chen & Jun Liu, 2022. "Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter," IJERPH, MDPI, vol. 19(3), pages 1-14, January.
    17. Lisa Singh & Leticia Bode & Ceren Budak & Kornraphop Kawintiranon & Colton Padden & Emily Vraga, 2020. "Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams," Journal of Computational Social Science, Springer, vol. 3(2), pages 343-366, November.
    18. Ross Schuchard & Andrew Crooks & Anthony Stefanidis & Arie Croitoru, 2019. "Bots fired: examining social bot evidence in online mass shooting conversations," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-12, December.
    19. Kelton Minor & Esteban Moro & Nick Obradovich, 2023. "Adverse weather amplifies social media activity," Papers 2302.08456, arXiv.org.
    20. Wen Shi & Diyi Liu & Jing Yang & Jing Zhang & Sanmei Wen & Jing Su, 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-18, 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:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00253-y. 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.