Combatting Mis/Disinformation: Combining Predictive Modeling and Machine Learning with Persuasion Science to Understand COVID-19 Vaccine Online Discourse

Main Article Content

Denise Scannell,, Ph.D. Linda Desens, Ph.D. David S.Day, Ph.D. Yolande Tra, Ph.D.


Health mis/disinformation can negatively impact health decisions and ultimately, health outcomes. Mis/disinformation related to COVID-19 vaccines has influenced vaccine hesitancy during a very critical time during the pandemic when globally, the vaccine was needed to attenuate the spread of the COVID-19 virus. This paper examines persuasive strategies used in Twitter posts, particular those with antivaccine sentiment. The authors developed a predictive model using variables based on the Elaboration Likelihood Model, Social Judgement Theory and the Extended Parallel Process Model to determine which persuasive tactics resulted in antivaccine, provaccine and neutral sentiment. The study also used machine learning to validate the persuasion variable algorithm to detect persuasion tactics in COVID-19 vaccine online discourse on Twitter. Understanding persuasive tactics used in antivaccine messaging can inform the development of a data-driven counter-response strategy.

Key words: Misinformation, Disinformation, Persuasion, Algorithm, Sentiment

Keywords: Misinformation, Disinformation, Persuasion, Algorithm, Sentiment

Article Details

How to Cite
SCANNELL,, Denise et al. Combatting Mis/Disinformation: Combining Predictive Modeling and Machine Learning with Persuasion Science to Understand COVID-19 Vaccine Online Discourse. Medical Research Archives, [S.l.], v. 10, n. 5, june 2022. ISSN 2375-1924. Available at: <>. Date accessed: 16 july 2024. doi:
Research Articles


1. Diggins FW. The true history of the discovery of penicillin, with refutation of the misinformation in the literature. Br J Biomed Sci. 1999;56(2):83-93.
2. Koslap-Petraco M. Vaccine hesitancy: Not a new phenomenon, but a new threat. J Am Assoc Nurse Pract. Nov 2019;31(11):624-626. doi:10.1097/jxx.0000000000000342
3. Scannell D, Desens L, Guadagno M, et al. COVID-19 Vaccine Discourse on Twitter: A Content Analysis of Persuasion Techniques, Sentiment and Mis/Disinformation. Journal of Health Communication. 2021/07/03 2021;26(7):443-459. doi:10.1080/10810730.2021.1955050
4. Krishna AT, Teresa L. . Misinformation About Health: A Review of Health Communication and Misinformation Scholarship. American Behavioral Scientist 2021;65(2):316-332.
5. Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018/03/09 2018;359(6380):1146-1151. doi:10.1126/science.aap9559
6. Kim DKD, Kreps GL. An Analysis of Government Communication in the United States During the COVID-19 Pandemic: Recommendations for Effective Government Health Risk Communication. World Med Health Policy. 2020:10.1002/wmh3.363. doi:10.1002/wmh3.363
7. Broniatowski D, Jamison A, Qi S, et al. Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. American Journal of Public Health. 2018;108(10):1378-84.
8. Organization WH. Infodemic. March 1, 2022, 2022. Accessed 03/01/2022, 2022.

9. Wilson SL, Wiysonge C. Social media and vaccine hesitancy. BMJ Global Health. 2020;5(10):e004206. doi:10.1136/bmjgh-2020-004206
10. Security H. Social Media Bots Overview. May 2018 2018;
11. Yuan X, Schuchard R, Crooks A. Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitte. Social Media + Society. April 2019;
12. Blankenship E, Goff M, Yin J, et al. Sentiment, Contents, and Retweets: A Study of Two Vaccine-Related Twitter Datasets. Permanente journal. 2018;22:17-138.
13. Berger J, Milkman KL. What Makes Online Content Viral? Journal of Marketing Research. 2012/04/01 2012;49(2):192-205. doi:10.1509/jmr.10.0353
14. Veale HJ, Sacks-Davis R, Weaver ERN, Pedrana AE, Stoové MA, Hellard ME. The use of social networking platforms for sexual health promotion: identifying key strategies for successful user engagement. BMC Public Health. 2015/02/06 2015;15(1):85. doi:10.1186/s12889-015-1396-z
15. Guidry JP, Carlyle K, Messner M, Jin Y. On pins and needles: how vaccines are portrayed on Pinterest. Vaccine. Sep 22 2015;33(39):5051-6. doi:10.1016/j.vaccine.2015.08.064
16. Krause RJ, Rucker DD. Strategic Storytelling: When Narratives Help Versus Hurt the Persuasive Power of Facts. Personality and Social Psychology Bulletin. 2020/02/01 2019;46(2):216-227. doi:10.1177/0146167219853845
17. Blankenship K, Wegener D. Opening the Mind to Close It: Considering a Message in Light of Important Values Increases Message Processing and Later Resistance to Change. Journal of Personality and Social Psychology. 03/01 2008;94:196-213. doi:10.1037/0022-3514.
18. Moran M, Lucas M, Everhart K, Morgan A, Prickett E. What makes anti-vaccine websites persuasive? A content analysis of techniques used by anti-vaccine websites to engender anti-vaccine sentiment. Journal of Communication in Healthcare. 10/03 2016;9:1-13. doi:10.1080/17538068.2016.1235531
19. Bester JC. Vaccine Refusal and Trust: The Trouble With Coercion and Education and Suggestions for a Cure. Journal of Bioethical Inquiry. 2015/12/01 2015;12(4):555-559. doi:10.1007/s11673-015-9673-1
20. Singh L, Bansal S, Bode L, et al. A first look at COVID-19 information and misinformation sharing on Twitter. ArXiv. 2020:arXiv:2003.13907v1.
21. School HK. Misinformation Review. Creative Commons Attribution 40 International (CC BY 40) 2020;1(8)
22. Alenezi MN, Alqenaei ZM. Machine Learning in Detecting COVID-19 Misinformation on Twitter. Future Internet. 2021;13(10):244.
23. Hayawi KS, S.; Serhani, M.A.; Ta;eb. O/,; Mathew, S. ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection. Public Health. 2022;203:23-30.
24. Hayawi K, Shahriar S, Serhani MA, Taleb I, Mathew SS. ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection. Public Health. 2022/02/01/ 2022;203:23-30. doi:
25. Krešňáková VM, Sarnovský M, Butka P. Deep learning methods for Fake News detection," 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo. 2019;
26. Choudrie J, Banerjee S, Kotecha K, Walambe R, Karende H, Ameta J. Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study. Computers in Human Behavior. 2021/06/01/ 2021;119:106716. doi:
27. Jamison A, Broniatowski DA, Smith MC, et al. Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter. American Journal of Public Health. 2020;110(S3):S331-S339. doi:10.2105/ajph.2020.305940
28. Sear R, Velaxquez N, Leahy R, et al. Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning. IEEE. 2020;8:91886-91893.
29. Hu T, Wang S, Luo W, et al. Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective. JOURNAL OF MEDICAL INTERNET RESEARCH. 2021;23(9):e30854.
30. Petty RE, Cacioppo JT. The elaboration likelihood model of persuasion. Communication and persuasion. Springer; 1986:1-24.
31. Smith SW, Atkin CK, Martell D, Allen R, Hembroff L. A social judgment theory approach to conducting formative research in a social norms campaign. Communication Theory. 2006;16(1):141-152.
32. Witte K, Allen M. A meta-analysis of fear appeals: Implications for effective public health campaigns. Health education & behavior. 2000;27(5):591-615.
33. Wongpakaran N, Wongpakaran T, Wedding D, Gwet KL. A comparison of Cohen’s Kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples. BMC Medical Research Methodology. 2013/04/29 2013;13(1):61. doi:10.1186/1471-2288-13-61
34. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Routledge; 2017.
35. Therneau T, Atkinson B, Ripley B. Rpart: Recursive Partitioning. R Package Version 4.1-3. 2022.
36. Liaw A, Wiener M. Classification and Regression by RandomForest. Forest. 11/30 2001;23
37. Joulin A, E. G, Bojanowski P, Mikolove T. Conference Proceedings. 2017:427-431.
38. Debut L, Sanh V, Chaumond J, et al. Transformers: State-of-the-Art Natural Language Processing. Association for Computational Linguistics; 2020:38-45.
39. Pérez JM, Giudici JC, Luque F. A Python Toolkit for Sentiment Analysis and SocialNLP tasks. arXiv:2106.09462v1 [cs.CL]
40. Hong H, Xiaoling G, Hua Y. Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest. 2016:219-224.
41. Aberdeen J, Bayer S, Yeniterzi R, et al. The MITRE Identification Scrubber Toolkit: design, training, and assessment. Int J Med Inform. Dec 2010;79(12):849-59. doi:10.1016/j.ijmedinf.2010.09.007
42. Abdelminaam DS, Ismail FH, Taha M, Taha A, Houssein EH, Nabil A. CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter. IEEE Access. 2021;9:27840-27867. doi:10.1109/access.2021.3058066
43. Cook J, Lewandowsky S, Ecker UKH. Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. Article. PLoS ONE. 2017;12(5):1-21. doi:10.1371/journal.pone.0175799
44. Scherer LD, Pennycook G. Who Is Susceptible to Online Health Misinformation? Am J Public Health. Oct 2020;110(S3):S276-s277. doi:10.2105/ajph.2020.305908
45. Ilona Kickbusch I, Pelikan, J.M., Apfel, F., Tsouros, A.D. (Eds.) Health Literacy—The Solid Facts. WHO Regional Office for Europe: Copenhagen, Denmark; 2013. Accessed April 8, 2022.
46. Promotion USDoHaHSOoDPaH. Health Literacy in Healthy People 2030.
47. Bin Naeem S, Kamel Boulos MN. COVID-19 Misinformation Online and Health Literacy: A Brief Overview. International journal of environmental research and public health. 2021;18(15):8091. doi:10.3390/ijerph18158091