IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i4p2159-d504074.html
   My bibliography  Save this article

Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter

Author

Listed:
  • Amir Karami

    (School of Information Science, University of South Carolina, Columbia, SC 29208, USA)

  • Morgan Lundy

    (School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA)

  • Frank Webb

    (Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USA)

  • Gabrielle Turner-McGrievy

    (Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA)

  • Brooke W. McKeever

    (School of Journalism and Mass Communications, University of South Carolina, Columbia, SC 29208, USA)

  • Robert McKeever

    (School of Journalism and Mass Communications, University of South Carolina, Columbia, SC 29208, USA)

Abstract

To combat health disinformation shared online, there is a need to identify and characterize the prevalence of topics shared by trolls managed by individuals to promote discord. The current literature is limited to a few health topics and dominated by vaccination. The goal of this study is to identify and analyze the breadth of health topics discussed by left (liberal) and right (conservative) Russian trolls on Twitter. We introduce an automated framework based on mixed methods including both computational and qualitative techniques. Results suggest that Russian trolls discussed 48 health-related topics, ranging from diet to abortion. Out of the 48 topics, there was a significant difference ( p -value ≤ 0.004) between left and right trolls based on 17 topics. Hillary Clinton’s health during the 2016 election was the most popular topic for right trolls, who discussed this topic significantly more than left trolls. Mental health was the most popular topic for left trolls, who discussed this topic significantly more than right trolls. This study shows that health disinformation is a global public health threat on social media for a considerable number of health topics. This study can be beneficial for researchers who are interested in political disinformation and health monitoring, communication, and promotion on social media by showing health information shared by Russian trolls.

Suggested Citation

  • Amir Karami & Morgan Lundy & Frank Webb & Gabrielle Turner-McGrievy & Brooke W. McKeever & Robert McKeever, 2021. "Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:2159-:d:504074
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/4/2159/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/4/2159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gabrielle Turner-McGrievy & Amir Karami & Courtney Monroe & Heather M. Brandt, 2020. "Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1035-1049, August.
    2. Amir Karami & London S. Bennett & Xiaoyun He, 2018. "Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 9(1), pages 18-28, January.
    3. Kim, Jae H. & Ji, Philip Inyeob, 2015. "Significance testing in empirical finance: A critical review and assessment," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 1-14.
    4. Heidi J. Larson, 2018. "The biggest pandemic risk? Viral misinformation," Nature, Nature, vol. 562(7727), pages 309-309, October.
    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. Aida El-Far Cardo & Thomas Kraus & Andrea Kaifie, 2021. "Factors That Shape People’s Attitudes towards the COVID-19 Pandemic in Germany—The Influence of MEDIA, Politics and Personal Characteristics," IJERPH, MDPI, vol. 18(15), pages 1-14, July.
    2. Zhijie Sasha Dong & Lingyu Meng & Lauren Christenson & Lawrence Fulton, 2021. "Social media information sharing for natural disaster response," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2077-2104, July.
    3. Kim, Jae H., 2017. "Stock returns and investors' mood: Good day sunshine or spurious correlation?," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 94-103.
    4. Michaelides, Michael, 2021. "Large sample size bias in empirical finance," Finance Research Letters, Elsevier, vol. 41(C).
    5. Torben E. Agergaard & Màiri E. Smith & Kristian H. Nielsen, 2020. "Vaccine Assemblages on Three HPV Vaccine-Critical Facebook Pages in Denmark from 2012 to 2019," Media and Communication, Cogitatio Press, vol. 8(2), pages 339-352.
    6. Marta R. Jabłońska & Karolina Zajdel & Radosław Zajdel, 2021. "Social and Psychological Consequences of COVID-19 Online Content at a Lockdown Phase—Europe and Asia Comparison," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    7. Philipp Lorenz-Spreen & Stephan Lewandowsky & Cass R. Sunstein & Ralph Hertwig, 2020. "How behavioural sciences can promote truth, autonomy and democratic discourse online," Nature Human Behaviour, Nature, vol. 4(11), pages 1102-1109, November.
    8. Rondan-Cataluña, F. Javier & Peral-Peral, Begoña & Ramírez-Correa, Patricio E., 2023. "Measuring public opinion of education apps," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    9. Jae H. Kim & Kamran Ahmed & Philip Inyeob Ji, 2018. "Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 524-546, December.
    10. Kim, Jae & Choi, In, 2015. "Unit Roots in Economic and Financial Time Series: A Re-Evaluation based on Enlightened Judgement," MPRA Paper 68411, University Library of Munich, Germany.
    11. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    12. Gabriel Miao Li & Josh Pasek & Jon A. Krosnick & Tobias H. Stark & Jennifer Agiesta & Gaurav Sood & Trevor Tompson & Wendy Gross, 2022. "Americans’ Attitudes toward the Affordable Care Act: What Role Do Beliefs Play?," The ANNALS of the American Academy of Political and Social Science, , vol. 700(1), pages 41-54, March.
    13. Amir Karami & Melek Yildiz Spinel & C. Nicole White & Kayla Ford & Suzanne Swan, 2021. "A Systematic Literature Review of Sexual Harassment Studies with Text Mining," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    14. Erdenebileg Batbaatar & Keun Ho Ryu, 2019. "Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    15. Todd Mitton, 2022. "Methodological Variation in Empirical Corporate Finance," The Review of Financial Studies, Society for Financial Studies, vol. 35(2), pages 527-575.
    16. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    17. Stephan B. Bruns & David I. Stern, 2019. "Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models," Empirical Economics, Springer, vol. 56(3), pages 797-830, March.
    18. Simandjuntak, Daniel P. & Jaenicke, Edward C. & Wrenn, Douglas H., 2022. "Heterogeneity in Consumer Food Stockpiling and Retailer Experiences During Hurricane Sandy," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322183, Agricultural and Applied Economics Association.
    19. Anghel, Dan Gabriel, 2021. "Data Snooping Bias in Tests of the Relative Performance of Multiple Forecasting Models," Journal of Banking & Finance, Elsevier, vol. 126(C).
    20. 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.

    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:gam:jijerp:v:18:y:2021:i:4:p:2159-:d:504074. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.