Understanding the landscape of web-based medical misinformation about vaccination.
Disinformation
Fuzzy trace theory
Google search terms
Health information
Inoculation information
Psycholinguistic properties of websites
Journal
Behavior research methods
ISSN: 1554-3528
Titre abrégé: Behav Res Methods
Pays: United States
ID NLM: 101244316
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
accepted:
17
03
2022
pubmed:
6
4
2022
medline:
15
2
2023
entrez:
5
4
2022
Statut:
ppublish
Résumé
Given the high rates of vaccine hesitancy, web-based medical misinformation about vaccination is a serious issue. We sought to understand the nature of Google searches leading to medical misinformation about vaccination, and guided by fuzzy-trace theory, the characteristics of misinformation pages related to comprehension, inference-making, and medical decision-making. We collected data from web pages presenting vaccination information. We assessed whether web pages presented medical misinformation, had an overarching gist, used narrative, and employed emotional appeals. We used Search Engine Optimization tools to determine the number of backlinks from other web pages, monthly Google traffic, and Google Keywords. We used Coh-Metrix to measure readability and Gist Inference Scores (GIS). For medical misinformation web pages, Google traffic and backlinks were heavily skewed with means of 138.8 visitors/month and 805 backlinks per page. Medical misinformation pages were significantly more likely than other vaccine pages to have backlinks from other pages, and significantly less likely to receive at least one visitor from Google searches per month. The top Google searches leading to medical misinformation were "the truth about vaccinations," "dangers of vaccination," and "pro con vaccines." Most frequently, pages challenged vaccine safety, with 32.7% having an overarching gist, 7.7% presenting narratives, and 17.3% making emotional appeals. Emotional appeals were significantly more common with medical misinformation than other high-traffic vaccination pages. Misinformation pages had a mean readability grade level of 11.5, and a mean GIS of - 0.234. Low GIS scores are a likely barrier to understanding gist, and are the "Achilles' heel" of misinformation pages.
Identifiants
pubmed: 35380412
doi: 10.3758/s13428-022-01840-5
pii: 10.3758/s13428-022-01840-5
pmc: PMC8981888
doi:
Substances chimiques
Vaccines
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
348-363Informations de copyright
© 2022. The Psychonomic Society, Inc.
Références
Ahrefs. Ahrefs by the numbers. https://Ahrefs.com/big-data . Accessed August 23, 2021.
Basch, C. H., Fera, J., & Garcia, P. (2019). Readability of influenza information online: implications for consumer health. American Journal of Infection Control, 47(11), 1298–1301. https://doi.org/10.1016/j.ajic.2019.04.178
doi: 10.1016/j.ajic.2019.04.178
pubmed: 31253552
Bernard, R., Bowsher, G., Sullivan, R., & Gibson-Fall, F. (2021). Disinformation and epidemics: Anticipating the next phase of biowarfare. Health Security, 19(1), 3–12. https://doi.org/10.1089/hs.2020.0038
doi: 10.1089/hs.2020.0038
pubmed: 33090030
pmcid: 9195489
Blalock, S. J., & Reyna, V. F. (2016). Using fuzzy-trace theory to understand and improve health judgments, decisions, and behaviors: A literature review. Health Psychology, 35, 781–792.
doi: 10.1037/hea0000384
pubmed: 27505197
pmcid: 4979567
Broniatowski, D. A., & Reyna, V. F. (2020). To illuminate and motivate: A fuzzy-trace model of the spread of information online. Computational and Mathematical Organization Theory, 26(4), 431–464. https://doi.org/10.1007/s10588-019-09297-2
doi: 10.1007/s10588-019-09297-2
pubmed: 33737859
Caldarelli, G., De Nicola, R., Petrocchi, M., Pratelli, M., & Saracco, F. (2021). Flow of online misinformation during the peak of the COVID-19 pandemic in Italy. EPJ Data Science, 10(1), 34.
doi: 10.1140/epjds/s13688-021-00289-4
pubmed: 34249599
pmcid: 8258478
Cedillos-Whynott, E. M., Wolfe, C. R., Widmer, C. L., Brust-Renck, P. G., Weil, A. M., & Reyna, V. F. (2016). The Effectiveness of argumentation in tutorial dialogues with an intelligent tutoring system. Behavior Research Methods, 48(1), 857–868. https://doi.org/10.3758/s13428-015-0681-1
doi: 10.3758/s13428-015-0681-1
pubmed: 26511370
pmcid: 5506373
Chin, C. L., Su, W. Y., & Chin, J. (2020). Representing the true and false text information about human papillomavirus vaccines. In Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 9, No. 1, pp. 317–321). SAGE Publications.
Clickstream (2021). ClickStream: The smarter SEO agency. https://clickstream.cc/ . Accessed August 13, 2021.
Coh-Metrix (2021). Accessed August 29, 2021 from http://141.225.61.35/cohmetrix2017
Coltheart, M. (1981). The MRC Psycholinguistic Database. Quarterly Journal of Experimental Psychology, 33A(1), 497–505. https://doi.org/10.1080/14640748108400805
doi: 10.1080/14640748108400805
Dandignac, M., & Wolfe, C. R. (2020). Gist Inference Scores predict gist memory for authentic patient education cancer texts. Patient Education and Counseling, 103(8), 1562–1567. https://doi.org/10.3758/s13428-015-0681-1
doi: 10.3758/s13428-015-0681-1
pubmed: 32098741
Duchsherer, A., Jason, M., Platt, C. A., & Majdik, Z. P. (2020). Immunized against science: Narrative community building among vaccine refusing/hesitant parents. Public Understanding of Science (Bristol, England), 29(4), 419–435. https://doi.org/10.1177/0963662520921537
doi: 10.1177/0963662520921537
pubmed: 32434461
Dwoskin, E. (2021). Facebook says post that cast doubt on COVID-19 vaccine was most popular on the platform from January through March. The Washington Post. Accessed August 23, 2021 from https://www.washingtonpost.com/technology/2021/08/21/facebook-coronavirus-vaccine/
Fagerlin, A., Wang, C., & Ubel, P. A. (2005). Reducing the influence of anecdotal reasoning on people’s health care decisions: Is a picture worth a thousand statistics? Medical Decision Making, 25(4), 398–405. https://doi.org/10.1177/0272989X05278931
doi: 10.1177/0272989X05278931
pubmed: 16061891
Frontline (2015). Jenny McCarthy: “We’re not an anti-vaccine movement… We’re pro-safe vaccine. Retrieved from the web on January 29, 2022 from https://www.pbs.org/wgbh/frontline/article/jenny-mccarthy-were-not-an-anti-vaccine-movement-were-pro-safe-vaccine/
Gwet, K. L. (2014). Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters. Advanced Analytics, LLC.
Hillyer, G. C., Beauchemin, M., Garcia, P., Kelsen, M., Brogan, F. L., Schwartz, G. K., & Basch, C. H. (2020). Readability of cancer clinical trials websites. Cancer Control, 27(1), 1–6. https://doi.org/10.1177/1073274819901125
doi: 10.1177/1073274819901125
Hussain, A., Ali, S., Ahmed, M., & Hussain, S. (2019). The anti-vaccination movement: a regression in modern medicine. Cureus, 10(7), e2919. https://doi.org/10.7759/cureus.2919
doi: 10.7759/cureus.2919
Jaiswal, J., & Halkitis, P. N. (2019). Towards a more inclusive and dynamic understanding of medical mistrust informed by science. Behavioral Medicine, 45(2), 79–85.
doi: 10.1080/08964289.2019.1619511
pubmed: 31343962
Jimenez, A. . V., Mesoudi, A., & Tehrani, J. J. (2020). No evidence that omission and confirmation biases affect the perception and recall of vaccine-related information. PLoS ONE, 15(3), e0228898. https://doi.org/10.1371/journal.pone.0228898
doi: 10.1371/journal.pone.0228898
pubmed: 32130217
pmcid: 7055885
Krishna, A., & Thompson, T. L. (2021). Misinformation about health: A review of health communication and misinformation scholarship. American Behavioral Scientist, 65(2), 316–332.
doi: 10.1177/0002764219878223
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
doi: 10.1038/nature14539
pubmed: 26017442
MacLean, S. A., Basch, C. H., Ethan, D., & Garcia, P. (2018). Readability of online information about HPV Immunization. Human Vaccines & Immunotherapeutics, 15(7–8), 1505–1507. https://doi.org/10.1080/21645515.2018.1502518
doi: 10.1080/21645515.2018.1502518
Madraki, G., Grasso, I.M., Otala, J., Liu, Y., & Matthews, J. (2021). Characterizing and comparing COVID-19 misinformation across languages, countries and platforms. In Companion Proceedings of the Web Conference 2021 (pp. 213–223).
Martino, J. (2020). CDC admits in federal court they have no evidence “vaccines don’t cause autism.” Retrieved from the web on January 29, 2022 from https://www.lewrockwell.com/2020/03/no_author/cdc-admits-in-federal-court-they-have-no-evidence-vaccines-dont-cause-autism/
McNamara, D. S., Graesser, A. C., McCarthy, P., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge University Press.
Pluviano, S., Watt, C., & Della Sala, S. (2017). Misinformation lingers in memory: Failure of three pro-vaccination strategies. PloS One, 12(7), 1–15. https://doi.org/10.1371/journal.pone.0181640
doi: 10.1371/journal.pone.0181640
Reyna, V. F. (2008). A theory of medical decision making and health: Fuzzy trace theory. Medical Decision Making, 28(1), 850–865. https://doi.org/10.1177/0272989X08327066
doi: 10.1177/0272989X08327066
pubmed: 19015287
pmcid: 2617718
Reyna, V. F. (2012). A new intuitionism: Meaning, memory, and development in fuzzy-trace theory. Judgment and Decision Making, 7(3), 332–359.
doi: 10.1017/S1930297500002291
pubmed: 25530822
pmcid: 4268540
Reyna, V. F. (2020). Of viruses, vaccines, and variability: Qualitative meaning matters. Trends in Cognitive Science, 24, 672–675.
doi: 10.1016/j.tics.2020.05.015
Reyna, V. F., Broniatowski, D. A., & Edelson, S. (2021). Viruses, Vaccines, and COVID-19: Explaining and Improving Risky Decision-making. Journal of Applied Research in Memory and Cognition, 10(4), 491–509.
doi: 10.1016/j.jarmac.2021.08.004
pubmed: 34926135
pmcid: 8668030
Rosenberg, H., Syed, S., & Rezaie, S. (2020). The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic. Canadian journal of emergency medicine, 22(4), 418–421.
doi: 10.1017/cem.2020.361
pubmed: 32248871
Rosselli, F., Skelly, J. J., & Mackie, D. M. (1995). Processing rational and emotional messages: The cognitive and affective mediation of persuasion. Journal of Experimental Social Psychology, 31, 163–190.
doi: 10.1006/jesp.1995.1008
Shaffer, V. A., Hulsey, L., & Zikmund-Fisher, B. J. (2013). The effects of process-focused versus experience-focused narratives in a breast cancer treatment decision task. Patient Education & Counseling, 93, 255–264.
doi: 10.1016/j.pec.2013.07.013
Stahl, J.-P., Cohen, R., Denis, F., Gaudelus, J., Martinot, A., Lery, T., & Lepetit, H. (2016). The impact of the web and social networks on vaccination. New challenges and opportunities offered to fight against vaccine hesitancy. Medecine et maladies infectieuses, 46, 117–122.
doi: 10.1016/j.medmal.2016.02.002
pubmed: 26987960
Statistica (2022a). Worldwide desktop market share of leading search engines from January 2010 to December 2021. Retrieved from the web on January 29, 2022 from https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/
Statistica (2022b). Market share of selected leading mobile search providers in the United States from October 2012 to September 2021. Retrieved from the web on January 29, 2022 from https://www.statista.com/statistics/511358/market-share-mobile-search-usa/
Studts, J. L., Ruberg, J. L., McGuffin, S. A., & Roetzer, L. M. (2010). Decisions to register for the National Marrow Donor Program: Rational vs emotional appeals. Bone Marrow Transplantation, 45(3), 422–428. https://doi.org/10.1038/bmt.2009.174
doi: 10.1038/bmt.2009.174
pubmed: 19648972
Tang, L., Fujimoto, K., Amith, M., Cunningham, R., Costantini, R. A., York, F., et al. (2021). “Down the rabbit hole” of vaccine misinformation on YouTube: Network exposure study. Journal of Medical Internet Research, 23(1), e23262. https://doi.org/10.2196/23262
doi: 10.2196/23262
pubmed: 33399543
pmcid: 7815449
Weil, M. A., & Wolfe, C. R. (2022). Individual differences in risk perception and misperception of COVID-19 in the context of political ideology. Applied Cognitive Psychology, 2022(36), 19–31. https://doi.org/10.1002/acp.3894
doi: 10.1002/acp.3894
Wolfe, C. R. (2021). Fuzzy-trace theory and the battle for the gist in the public mind. Journal of Applied Research in Memory and Cognition, 10, 527–531.
doi: 10.1016/j.jarmac.2021.10.004
pubmed: 34926137
pmcid: 8668039
Wolfe, C. R. (2012). Individual differences in the "MySide bias" in reasoning and written argumentation. Written Communication, 29, 474–498.
doi: 10.1177/0741088312457909
Wolfe, C. R., Britt, M. A., & Butler, J. A. (2009). Argumentation schema and the myside bias in written argumentation. Written Communication, 26, 183–209.
doi: 10.1177/0741088309333019
Wolfe, C. R., & Dandignac, M. (2021). Revising flash fiction for Coh-Metrix: Experiential learning with discourse technologies. Journal on Excellence in College Teaching, 32(1), 123–140 http://celt.miamioh.edu/ject/issue.php?v=32&n=1
Wolfe, C. R., Dandignac, M., & Reyna, V. F. (2019a). A theoretically motivated method for automatically evaluating texts for gist inferences. Behavior Research Methods, 51(6), 2419–2437. https://doi.org/10.3758/s13428-019-01284-4
doi: 10.3758/s13428-019-01284-4
pubmed: 31342470
Wolfe, C. R., Dandignac, M., Sullivan, R., Moleski, T., & Reyna, V. F. (2019b). Automatic evaluation of cancer treatment texts for gist inferences and comprehension. Medical Decision Making, 39(8), 939–949. https://doi.org/10.1177/0272989X19874316s
doi: 10.1177/0272989X19874316s
pubmed: 31556801
Wolfe, C. R., Dandignac, M., Wang, C., & Lowe, S. R. (2021). Gist Inference Scores predict cloze comprehension “in your own words” for native, not ESL readers. Health Communication, early online access, https://doi.org/10.1080/10410236.2021.1920690