Research Article

Developing Fake News Immunity: Fallacies as Misinformation Triggers During the Pandemic

Elena Musi 1 * , Myrto Aloumpi 2 , Elinor Carmi 3 , Simeon Yates 1 , Kay O’Halloran 1
More Detail
1 Department of Communication and Media, University of Liverpool, Liverpool, UK2 Department of Philology, University of Crete, GREECE3 Department of Sociology, City University London, London, UK* Corresponding Author
Online Journal of Communication and Media Technologies, 12(3), July 2022, e202217, https://doi.org/10.30935/ojcmt/12083
Published: 12 May 2022
OPEN ACCESS   3192 Views   1475 Downloads
Download Full Text (PDF)

ABSTRACT

Misinformation constitutes one of the main challenges to counter the infodemic: misleading news, even if not blatantly false, can cause harm especially in crisis scenarios such as the pandemic. Due to the fast proliferation of information across digital media, human fact-checkers struggle to keep up with fake news, while automatic fact-checkers are not able to identify the grey area of misinformation. We, thus, propose to reverse engineer the manipulation of information offering citizens the means to become their own fact-checkers through digital literacy and critical thinking. Through a corpus analysis of fact-checked news about COVID-19, we identify 10 fallacies–arguments which seem valid but are not–that systematically trigger misinformation and offer a systematic procedure to identify them. Next to fallacies, we examine the types of sources associated to (mis-/dis-)information in our dataset as well as the type of claims making up the headlines. The statistical patterns surfaced from these three levels of analysis reveal a misinformation ecosystem where no source type is exempt from flawed arguments with frequent evading the burden of proof and cherry picking behaviors, even when descriptive claims are at stake. In such a scenario, exercising the audience’s critical skills through fallacy and semantic analysis is necessary to guarantee fake news immunity.
Keywords: misinformation, fallacy theory, digital literacy, fact-checking, multi-level annotation

CITATION (APA)

Musi, E., Aloumpi, M., Carmi, E., Yates, S., & O’Halloran, K. (2022). Developing Fake News Immunity: Fallacies as Misinformation Triggers During the Pandemic. Online Journal of Communication and Media Technologies, 12(3), e202217. https://doi.org/10.30935/ojcmt/12083

REFERENCES

  1. Abdulai, A. F., Tiffere, A. H., Adam, F., & Kabanunye, M. M. (2020). COVID-19 information-related digital literacy among online health consumers in a low-income country. International Journal of Medical Informatics, 145, 104322. https://doi.org/10.1016/j.ijmedinf.2020.104322
  2. Allington, D., Duffy, B., Wessely, S., Dhavan, N., & Rubin, J. (2020). Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency. Psychological Medicine, 51(10), 1763-1769. https://doi.org/10.1017/s0033291721000593
  3. Amazeen, M. A., & Bucy, E. P. (2019). Conferring resistance to digital disinformation: The inoculating influence of procedural news knowledge. Journal of Broadcasting & Electronic Media, 63(3), 415-432. https://doi.org/10.1080/08838151.2019.1653101
  4. Archila, P. A., Danies, G., Molina, J., Truscott de Mejía, A. M., & Restrepo, S. (2021). Towards Covid-19 literacy. Science & Education, 30(4), 785-808. https://doi.org/10.1007/s11191-021-00222-1
  5. Aufderheide, P. (1993). Media literacy. A report of the national leadership conference on media literacy. Aspen Institute. http://files.eric.ed.gov/fulltext/ED365294.pdf
  6. Beaunoyer, E., Dupéré, S., & Guitton, M. J. (2020). COVID-19 and digital inequalities: Reciprocal impacts and mitigation strategies. Computers in Human Behavior, 111, 106424. https://doi.org/10.1016/j.chb.2020.106424
  7. Boudry, M., Paglieri, F., & Pigliucci, M. (2015). The fake, the flimsy, and the fallacious: Demarcating arguments in real life. Argumentation, 29(4), 431-456. https://doi.org/10.1007/s10503-015-9359-1
  8. Brennen, J. S., Simon, F., Howard, P. N., & Nielsen, R. K. (2020). Types, sources, and claims of COVID-19 misinformation. Reuters Institute. http://www.primaonline.it/wp-content/uploads/2020/04/COVID-19_reuters.pdf
  9. Carmi, E., Yates, S. J., Lockley, E., & Pawluczuk, A. (2020). Data citizenship: Rethinking data literacy in the age of disinformation, misinformation, and malinformation. Internet Policy Review, 9(2). https://doi.org/10.14763/2020.2.1481
  10. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
  11. Fletcher, R., Kalogeropoulos, A., Simon, F. M., & Nielsen, R. K. (2020). Information inequality in the UK coronavirus communications crisis. Reuters Institute. https://reutersinstitute.politics.ox.ac.uk/information-inequality-uk-coronavirus-communications-crisis
  12. Hamblin, C. L. (2022, March). Fallacies. Advanced Reasoning Forum.
  13. Hansen, H. V. (1996). Aristotle, Whately, and the taxonomy of fallacies. In Proceedings of the International Conference on Formal and Applied Practical Reasoning (pp. 318-330). Springer. https://doi.org/10.1007/3-540-61313-7_82
  14. Herring, S. C. (2007). A faceted classification scheme for computer-mediated discourse. Language@ Internet, 4, 1.
  15. Hidey, C., Musi, E., Hwang, A., Muresan, S., & McKeown, K. (2017, September). Analyzing the semantic types of claims and premises in an online persuasive forum. In Proceedings of the 4th Workshop on Argument Mining (pp. 11-21). https://doi.org/10.18653/v1/W17-5102
  16. Hobbs, R. (2010). Digital and media literacy: A plan of action. A white paper on the digital and media literacy recommendations of the Knight Commission on the information needs of communities in a democracy. Aspen Institute. https://www.aspeninstitute.org/wp-content/uploads/2010/11/Digital_and_Media_
  17. Literacy.pdf
  18. Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., & Schölkopf, B. (2022). Logical fallacy detection. arXiv:2202.13758. https://doi.org/10.48550/arXiv.2202.13758
  19. Kahne, J., & Bowyer, B. (2017). Educating for democracy in a partisan age: Confronting the challenges of motivated reasoning and misinformation. American Educational Research Journal, 54(1), 3-34. https://doi.org/10.3102/0002831216679817
  20. Kasprak, A. (2020, December 10). No, mRNA COVID-19 vaccines do not ‘alter your DNA’ [fact-check of the news claim mRNA vaccines are capable of altering or damaging human DNA]. Snopes. https://www.snopes.com/fact-check/mrna-alter-dna/
  21. Kilpatrick, A., & Fefferman, N. (2020, June 8). People who do not show COVID-19 symptoms can and do transmit it to others; physical distancing and face masks effectively reduce the risk of transmission [fact-check of claims on Facebook the WHO stated that asymptomatic spread of COVID-19 is “very rare”, therefore physical distancing and face masks are not necessary]. Healthfeedback.org. https://healthfeedback.org/claimreview/people-who-do-not-show-covid-19-symptoms-can-and-do-transmit-it-to-others-physical-distancing-and-face-masks-effectively-reduce-the-risk-of-transmission/
  22. Koltay, T. (2011). The media and the literacies: Media literacy, information literacy, digital literacy. Media, Culture & Society, 33(2), 211-221. https://doi.org/10.1177/0163443710393382
  23. Kyriakidou, M., Morani, M., Soo, N., & Cushion, S. (2020). Government and media misinformation about COVID-19 is confusing the public. LSE COVID-19 Blog. https://blogs.lse.ac.uk/covid19/2020/05/07/government-and-media-misinformation-about-covid-19-is-confusing-the-public/
  24. Li, H. O. Y., Bailey, A., Huynh, D., & Chan, J. (2020). YouTube as a source of information on COVID-19: A pandemic of misinformation? BMJ Global Health, 5(5), e002604. https://doi.org/10.1136/bmjgh-2020-002604
  25. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
  26. Mahid, Z. I., Manickam, S., & Karuppayah, S. (2018, October). Fake news on social media: Brief review on detection techniques. In Proceedings of the 2018 4th International Conference on Advances in Computing, Communication & Automation (pp. 1-5). IEEE. https://doi.org/10.1109/icaccaf.2018.8776689
  27. Musi, E., Ghosh, D., & Muresan, S. (2016, August). Towards feasible guidelines for the annotation of argument schemes. In Proceedings of the 3rd Workshop on Argument Mining (pp. 82-93). https://doi.org/10.18653/v1/W16-2810
  28. Song, Y., Heilman, M., Klebanov, B. B., & Deane, P. (2014, June). Applying argumentation schemes for essay scoring. In Proceedings of the 1st Workshop on Argumentation Mining (pp. 69-78). https://doi.org/10.3115/v1/W14-2110
  29. Tandoc Jr, E. C., Lim, Z. W., & Ling, R. (2018). Defining “fake news” A typology of scholarly definitions. Digital Journalism, 6(2), 137-153. https://doi.org/10.17645/mac.v9i1.3331
  30. Teoh, F. (2020a, August 4). Health authorities encourage the general public to wear face masks to reduce COVID-19 transmission [fact-check of claims on Facebook health authorities like the World Health Organization and the US Centers for Disease Control and Prevention discourage people from wearing face masks]. Healthfeedback.org. https://healthfeedback.org/claimreview/health-authorities-encourage-the-general-public-to-wear-face-masks-to-reduce-Covid-19-transmission/
  31. Teoh, F. (2020b, December 19). The US national childhood vaccine injury act does not stop people from suing vaccine manufacturers [fact-check of the webpage https://archive.ph/OYp8S]. Healthfeedback.org. https://healthfeedback.org/claimreview/the-u-s-national-childhood-vaccine-injury-act-does-not-stop-people-from-suing-vaccine-manufacturers/
  32. Thorne, J., & Vlachos, A. (2018). Automated fact checking: Task formulations, methods and future directions. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 3346–3359), Santa Fe, New Mexico, USA. Association for Computational Linguistics.
  33. Tindale, C. W. (2007). Fallacies and argument appraisal. Cambridge University Press. https://doi.org/10.1017/CBO9780511806544
  34. van Eemeren, F. H, & Grootendorst, R. (2004). A systematic theory of argumentation: The pragma-dialectical approach. Cambridge University Press. https://doi.org/10.1017/CBO9780511616389
  35. Vraga, E. K., Bode, L., & Tully, M. (2020). Creating news literacy messages to enhance expert corrections of misinformation on Twitter. Communication Research, 49(2), 0093650219898094. https://doi.org/10.1177/0093650219898094
  36. Yates, S. J., & Sumner, T. R. (1997, January). Digital genres and the new burden of fixity. In Proceedings of the 13th Hawaii International Conference on System Sciences (pp. 3-12). IEEE.
  37. Yates, S., Carmi, E., Pawluczuk, A., Lockley, E., Wessels, B., & Gangneux, J. (2020). Me and my big data report 2020: Understanding citizens’ data literacies: thinking, doing & participating with our data. University of Liverpool. https://www.liverpool.ac.uk/humanities-and-social-sciences/research/research-themes/centre-for-digital-humanities/projects/big-data/