IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v5y2022i2d10.1007_s42001-022-00173-9.html
   My bibliography  Save this article

Comparative analysis of social bots and humans during the COVID-19 pandemic

Author

Listed:
  • Ho-Chun Herbert Chang

    (University of Southern California
    Information Science Institute)

  • Emilio Ferrara

    (Information Science Institute
    USC)

Abstract

Using more than 4 billion tweets and labels on more than 5 million users, this paper compares the behavior of humans and bots politically and semantically during the pandemic. Results reveal liberal bots are more central than humans in general, but less important than institutional humans as the elite circle grows smaller. Conservative bots are surprisingly absent when compared to prior work on political discourse, but are better than liberal bots at eliciting replies from humans, which suggest they may be perceived as human more frequently. In terms of topic and framing, conservative humans and bots disproportionately tweet about the Bill Gates and bio-weapons conspiracy, whereas the 5G conspiracy is bipartisan. Conservative humans selectively ignore mask-wearing and we observe prevalent out-group tweeting when discussing policy. We discuss and contrast how humans appear more centralized in health-related discourse as compared to political events, which suggests the importance of credibility and authenticity for public health in online information diffusion.

Suggested Citation

  • Ho-Chun Herbert Chang & Emilio Ferrara, 2022. "Comparative analysis of social bots and humans during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 5(2), pages 1409-1425, November.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:2:d:10.1007_s42001-022-00173-9
    DOI: 10.1007/s42001-022-00173-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-022-00173-9
    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/s42001-022-00173-9?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. Druckman, James N. & Peterson, Erik & Slothuus, Rune, 2013. "How Elite Partisan Polarization Affects Public Opinion Formation," American Political Science Review, Cambridge University Press, vol. 107(1), pages 57-79, February.
    2. Sandra González-Bailón & Manlio De Domenico, 2021. "Bots are less central than verified accounts during contentious political events," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(11), pages 2013443118-, March.
    3. Bjarke Mønsted & Piotr Sapieżyński & Emilio Ferrara & Sune Lehmann, 2017. "Evidence of complex contagion of information in social media: An experiment using Twitter bots," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-12, September.
    4. 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.
    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. Zixuan Weng & Aijun Lin, 2022. "Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(24), pages 1-17, December.

    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. Yevgeniy Golovchenko, 2020. "Measuring the scope of pro-Kremlin disinformation on Twitter," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-11, December.
    2. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    3. Ciprian-Octavian Truică & Elena-Simona Apostol, 2022. "MisRoBÆRTa: Transformers versus Misinformation," Mathematics, MDPI, vol. 10(4), pages 1-25, February.
    4. Carlos Carrasco-Farré, 2022. "The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-18, December.
    5. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    6. Uğur Baloğlu, 2021. "Trolls, Pressure and Agenda: The discursive fight on Twitter in Turkey," Media and Communication, Cogitatio Press, vol. 9(4), pages 39-51.
    7. Schläpfer, Felix, 2016. "Democratic valuation (DV): Using majority voting principles to value public services," Ecological Economics, Elsevier, vol. 122(C), pages 36-42.
    8. Bloemraad, Irene & Voss, Kim & Silva, Fabiana, 2014. "Framing the Immigrant Movement as about Rights, Family, or Economics: Which Appeals Resonate and for Whom?," Institute for Research on Labor and Employment, Working Paper Series qt3b32w33p, Institute of Industrial Relations, UC Berkeley.
    9. Mujtaba Ali Isani, 2021. "Methodological Problems of Using Arabic-Language Twitter as a Gauge for Arab Attitudes Toward Politics and Society," Contemporary Review of the Middle East, , vol. 8(1), pages 22-35, March.
    10. Arora, Swapan Deep & Singh, Guninder Pal & Chakraborty, Anirban & Maity, Moutusy, 2022. "Polarization and social media: A systematic review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Nie, Yanyi & Li, Wenyao & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Markovian approach to tackle competing pathogens in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    12. Onur Altındağ & Neeraj Kaushal, 2021. "Do refugees impact voting behavior in the host country? Evidence from Syrian refugee inflows to Turkey," Public Choice, Springer, vol. 186(1), pages 149-178, January.
    13. Joshua Alley, 2023. "Elite Cues and Public Attitudes Towards Military Alliances," Journal of Conflict Resolution, Peace Science Society (International), vol. 67(7-8), pages 1537-1563, August.
    14. Nwaibeh, E.A. & Chikwendu, C.R., 2023. "A deterministic model of the spread of scam rumor and its numerical simulations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 111-129.
    15. Li, WenYao & Xue, Xiaoyu & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Competing spreading dynamics in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    16. Cohle, Zachary & Ortega, Alberto, 2022. "Life of the party: The polarizing effect of foreign direct investment," European Journal of Political Economy, Elsevier, vol. 72(C).
    17. Wanyun Shao & Feng Hao, 2020. "Approval of political leaders can slant evaluation of political issues: evidence from public concern for climate change in the USA," Climatic Change, Springer, vol. 158(2), pages 201-212, January.
    18. Elizabeth Suhay & James N. Druckman, 2015. "The Politics of Science," The ANNALS of the American Academy of Political and Social Science, , vol. 658(1), pages 6-15, March.
    19. Eminente, Clara & Artime, Oriol & De Domenico, Manlio, 2022. "Interplay between exogenous triggers and endogenous behavioral changes in contagion processes on social networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    20. Monika Pompeo & Nina Serdarevic, 2021. "Is information enough? The case of Republicans and climate change," Discussion Papers 2021-08, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.

    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:5:y:2022:i:2:d:10.1007_s42001-022-00173-9. 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.