Believe it or not: A network analysis investigating how individuals embrace false and true statements during COVID-19

References

• Ahn, W., Brewer, W. F., & Mooney, R. J. (1992). Schema acquisition from a single example. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(2), 391–412. https://doi.org/10.1037/0278-7393.18.2.391
   Web of Science ®Google Scholar
• Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211
   Web of Science ®Google Scholar
• Armstrong, K., Rose, A., Peters, N., Long, J. A., McMurphy, S., & Shea, J. A. (2006). Distrust of the health care system and self-reported health in the United States. Journal of General Internal Medicine, 21(4), 292–297. https://doi.org/10.1111/j.1525-1497.2006.00396.x
   PubMed Web of Science ®Google Scholar
• Boutyline, A., & Vaisey, S. (2017). Belief network analysis: A relational approach to understanding the structure of attitudes. American Journal of Sociology, 122(5), 1371–1447. https://doi.org/10.1086/691274
   Web of Science ®Google Scholar
• Brandt, M. J. (2022). Measuring the belief system of a person. Journal of Personality and Social Psychology, 123(4), 830–853. https://doi.org/10.1037/pspp0000416
   PubMed Web of Science ®Google Scholar
• Brandt, M. J., Sibley, C. G., & Osborne, D. (2019). What is central to political belief system networks? Personality and Social Psychology Bulletin, 45(9), 1352–1364. https://doi.org/10.1177/0146167218824354
   PubMed Web of Science ®Google Scholar
• Brandt, M. J., & Sleegers, W. W. A. (2021). Evaluating belief system networks as a theory of political belief system dynamics. Personality and Social Psychology Review, 25(2), 159–185. https://doi.org/10.1177/1088868321993751
   PubMed Web of Science ®Google Scholar
• Bridgman, A., Merkley, E., Loewen, P. J., Owen, T., Ruths, D., Teichmann, L., & Zhilin, O. (2020). The causes and consequences of COVID-19 misperceptions: Understanding the role of news and social media. Harvard Kennedy School Misinformation Review, 1(3). https://doi.org/10.37016/mr-2020-028
   PubMedGoogle Scholar
• Bronstein, M. V., Pennycook, G., Bear, A., Rand, D. G., & Cannon, T. D. (2019). Belief in fake news is associated with delusionality, dogmatism, religious fundamentalism, and reduced analytic thinking. Journal of Applied Research in Memory and Cognition, 8(1), 108–117. https://doi.org/10.1037/h0101832
   Web of Science ®Google Scholar
• Bruine de Bruin, W., Saw, H.-W., & Goldman, D. P. (2020). Political polarization in US residents’ COVID-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty, 61(2), 177–194. https://doi.org/10.1007/s11166-020-09336-3
   PubMed Web of Science ®Google Scholar
• Calvillo, D. P., Ross, B. J., Garcia, R. J. B., Smelter, T. J., & Rutchick, A. M. (2020). Political ideology predicts perceptions of the threat of COVID-19 (and susceptibility to fake news about it). Social Psychological and Personality Science, 11(8), 1119–1128. https://doi.org/10.1177/1948550620940539
   Web of Science ®Google Scholar
• Carnahan, D., Ulusoy, E., Barry, R., McGraw, J., Virtue, I., & Bergan, D. E. (2022). What should I believe? A conjoint analysis of the influence of message characteristics on belief in, perceived credibility of, and intent to share political posts. Journal of Communication, 72(5), 592–603. https://doi.org/10.1093/joc/jqac023
   Web of Science ®Google Scholar
• Centers for Disease Control and Prevention. (2020). United States COVID-19 cases and deaths by state over time. https://data.cdc.gov/Case-Surveillance/United-States-COVID-19-Cases-and-Deaths-by-State-o/9mfq-cb36
   Google Scholar
• Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6), 066111. https://doi.org/10.1103/PhysRevE.70.066111
   PubMed Web of Science ®Google Scholar
• Dalege, J., & van der Does, T. (2022). Using a cognitive network model of moral and social beliefs to explain belief change. Science Advances, 8(33), eabm0137. https://doi.org/10.1126/sciadv.abm0137
   PubMed Web of Science ®Google Scholar
• Damstra, A., Vliegenthart, R., Boomgaarden, H., Glüer, K., Lindgren, E., Strömbäck, J., & Tsfati, Y. (2023). Knowledge and the news: An investigation of the relation between news use, news avoidance, and the presence of (mis)beliefs. The International Journal of Press/Politics, 28(1), 29–48. https://doi.org/10.1177/19401612211031457
   Web of Science ®Google Scholar
• Dechêne, A., Stahl, C., Hansen, J., & Wänke, M. (2010). The truth about the truth: A meta-analytic review of the truth effect. Personality and Social Psychology Review, 14(2), 238–257. https://doi.org/10.1177/1088868309352251
   PubMed Web of Science ®Google Scholar
• Ecker, U. K. H., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., & Amazeen, M. A. (2022). The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1), 13–29. https://doi.org/10.1038/s44159-021-00006-y
   Google Scholar
• Fishman, N., & Davis, N. T. (2022). Change we can believe in: Structural and content dynamics within belief networks. American Journal of Political Science, 66(3), 648–663. https://doi.org/10.1111/ajps.12626
   Web of Science ®Google Scholar
• Gardner, W., Mulvey, E. P., & Shaw, E. C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychological Bulletin, 118(3), 392–404. https://doi.org/10.1037/0033-2909.118.3.392
   PubMed Web of Science ®Google Scholar
• Garrett, R. K., & Bond, R. M. (2021). Conservatives’ susceptibility to political misperceptions. Science Advances, 7(23), eabf1234. https://doi.org/10.1126/sciadv.abf1234
   PubMed Web of Science ®Google Scholar
• Garrett, R. K., & Weeks, B. E. (2017). Epistemic beliefs’ role in promoting misperceptions and conspiracist ideation. PLoS ONE, 12(9), e0184733. https://doi.org/10.1371/journal.pone.0184733
   PubMed Web of Science ®Google Scholar
• Ghosh, V. E., & Gilboa, A. (2014). What is a memory schema? A historical perspective on current neuroscience literature. Neuropsychologia, 53, 104–114. https://doi.org/10.1016/j.neuropsychologia.2013.11.010
   PubMed Web of Science ®Google Scholar
• Graesser, A. C., & Nakamura, G. V. (1982). The impact of a schema on comprehension and memory. In G. H. Bower (Ed.), Psychology of learning and motivation (Vol. 16, pp. 59–109). Academic Press. https://doi.org/10.1016/S0079-7421(08)60547-2
   Google Scholar
• Gruzd, A., & Mai, P. (2020). Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter. Big Data & Society, 7(2). https://doi.org/10.1177/2053951720938405
   Web of Science ®Google Scholar
• Hammond, D. K., Gur, Y., & Johnson, C. R. (2013). Graph diffusion distance: A difference measure for weighted graphs based on the graph Laplacian exponential kernel. Proceedings of IEEE GlobalSIP, 419–422. https://doi.org/10.1109/GlobalSIP.2013.6736904
   Google Scholar
• Huang, Y., & Yang, C. (2020). A metacognitive approach to reconsidering risk perceptions and uncertainty: Understand information seeking during COVID-19. Science Communication, 42(5), 616–642. https://doi.org/10.1177/1075547020959818
   Web of Science ®Google Scholar
• Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). Ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software, 24(3), nihpa54860. https://doi.org/10.18637/jss.v024.i03
   PubMedGoogle Scholar
• Johns Hopkins Medicine. (n.d.). Covid-19 – myth versus fact. https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/2019-novel-coronavirus-myth-versus-fact
   Google Scholar
• Jost, J. T. (2017). Ideological asymmetries and the essence of political psychology. Political Psychology, 38(2), 167–208. https://doi.org/10.1111/pops.12407
   Web of Science ®Google Scholar
• Jost, J. T., Federico, C. M., & Napier, J. L. (2009). Political ideology: Its structure, functions, and elective affinities. Annual Review of Psychology, 60(1), 307–337. https://doi.org/10.1146/annurev.psych.60.110707.163600
   PubMed Web of Science ®Google Scholar
• Jost, J. T., Glaser, J., Kruglanski, A. W., & Sulloway, F. J. (2003). Political conservatism as motivated social cognition. Psychological Bulletin, 129(3), 339–375. https://doi.org/10.1037/0033-2909.129.3.339
   PubMed Web of Science ®Google Scholar
• Kruglanski, A. W. (1989). The psychology of being “right”: The problem of accuracy in social perception and cognition. Psychological Bulletin, 106(3), 395–409. https://doi.org/10.1037/0033-2909.106.3.395
   Web of Science ®Google Scholar
• Kruglanski, A. W., & Ajzen, I. (1983). Bias and error in human judgment. European Journal of Social Psychology, 13(1), 1–44. https://doi.org/10.1002/ejsp.2420130102
   Web of Science ®Google Scholar
• Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480–498. https://doi.org/10.1037/0033-2909.108.3.480
   PubMed Web of Science ®Google Scholar
• Lee, S., Peng, T.-Q., Lapinski, M. K., Turner, M. M., Jang, Y., & Schaaf, A. (2021). Too stringent or too Lenient: Antecedents and consequences of perceived stringency of COVID-19 policies in the United States. Health Policy OPEN, 2, 100047. https://doi.org/10.1016/j.hpopen.2021.100047
   PubMedGoogle Scholar
• Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106–131. https://doi.org/10.1177/1529100612451018
   PubMed Web of Science ®Google Scholar
• Maglić, M., Pavlović, T., & Franc, R. (2021). Analytic thinking and political orientation in the corona crisis. Frontiers in Psychology, 12, 631800. https://doi.org/10.3389/fpsyg.2021.631800
   PubMed Web of Science ®Google Scholar
• Miller, J. M. (2020). Psychological, political, and situational factors combine to boost COVID-19 conspiracy theory beliefs. Canadian Journal of Political Science, 53(2), 327–334. https://doi.org/10.1017/S000842392000058X
   Web of Science ®Google Scholar
• Nan, X., Thier, K., & Wang, Y. (2023). Health misinformation: What it is, why people believe it, how to counter it. Annals of the International Communication Association, 47(4), 381–410. https://doi.org/10.1080/23808985.2023.2225489
   Google Scholar
• Nan, X., Wang, Y., & Thier, K. (2022). Why do people believe health misinformation and who is at risk? A systematic review of individual differences in susceptibility to health misinformation. Social Science & Medicine, 314, 115398. https://doi.org/10.1016/j.socscimed.2022.115398
   PubMed Web of Science ®Google Scholar
• Newman, M. E. J. (2003). Mixing patterns in networks. Physical Review E, 67(2), 026126. https://doi.org/10.1103/PhysRevE.67.026126
   Web of Science ®Google Scholar
• Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303–330. https://doi.org/10.1007/s11109-010-9112-2
   Web of Science ®Google Scholar
• Peeples, L. (2020). What the data say about wearing face masks. Nature, 586(7828), 186–189. https://doi.org/10.1038/d41586-020-02801-8
   PubMed Web of Science ®Google Scholar
• Quackenbush, R. L. (1989). Comparison and contrast between belief system theory and cognitive theory. The Journal of Psychology, 123(4), 315–328. https://doi.org/10.1080/00223980.1989.10542988
   Web of Science ®Google Scholar
• Salvi, C., Iannello, P., Cancer, A., McClay, M., Rago, S., Dunsmoor, J. E., & Antonietti, A. (2021). Going viral: How fear, socio-cognitive polarization and problem-solving influence fake news detection and proliferation during COVID-19 pandemic. Frontiers in Communication, 5, 562588. https://doi.org/10.3389/fcomm.2020.562588
   Google Scholar
• Scheufele, D. A., & Krause, N. M. (2019). Science audiences, misinformation, and fake news. Proceedings of the National Academy of Sciences, 116(16), 7662–7669. https://doi.org/10.1073/pnas.1805871115
   PubMed Web of Science ®Google Scholar
• Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9–45. https://doi.org/10.18608/jla.2016.33.3
   Google Scholar
• Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., Frank, K., Rupp, A. A., & Mislevy, R. (2009). Epistemic network analysis: A prototype for 21st-century assessment of learning. International Journal of Learning and Media, 1(2), 33–53. https://doi.org/10.1162/ijlm.2009.0013
   Google Scholar
• Stall, L. M., & Petrocelli, J. V. (2023). Countering conspiracy theory beliefs: Understanding the conjunction fallacy and considering disconfirming evidence. Applied Cognitive Psychology, 37(2), 266–276. https://doi.org/10.1002/acp.3998
   Web of Science ®Google Scholar
• Stone, A. R., & Marsh, E. J. (2023). Belief in COVID-19 misinformation: Hopeful claims are rated as truer. Applied Cognitive Psychology, 37(2), 399–408. https://doi.org/10.1002/acp.4042
   Web of Science ®Google Scholar
• van der Linden, S., Thompson, B., & Roozenbeek, J. (2023). Editorial – the truth is out there: The psychology of conspiracy theories and how to counter them. Applied Cognitive Psychology, 37(2), 252–255. https://doi.org/10.1002/acp.4054
   Web of Science ®Google Scholar
• van Prooijen, J.-W., & Douglas, K. M. (2017). Conspiracy theories as part of history: The role of societal crisis situations. Memory Studies, 10(3), 323–333. https://doi.org/10.1177/1750698017701615
   PubMed Web of Science ®Google Scholar
• Vitriol, J. A., & Marsh, J. K. (2021). A pandemic of misbelief: How beliefs promote or undermine COVID-19 mitigation. Frontiers in Political Science, 3. https://doi.org/10.3389/fpos.2021.648082
   PubMedGoogle Scholar
• Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559
   PubMed Web of Science ®Google Scholar
• Walter, N., Brooks, J. J., Saucier, C. J., & Suresh, S. (2021). Evaluating the impact of attempts to correct health misinformation on social media: A meta-analysis. Health Communication, 36(13), 1776–1784. https://doi.org/10.1080/10410236.2020.1794553
   PubMed Web of Science ®Google Scholar
• Walter, N., & Murphy, S. T. (2018). How to unring the bell: A meta-analytic approach to correction of misinformation. Communication Monographs, 85(3), 423–441. https://doi.org/10.1080/03637751.2018.1467564
   Web of Science ®Google Scholar
• Walter, N., & Tukachinsky, R. (2020). A meta-analytic examination of the continued influence of misinformation in the face of correction: How powerful is it, why does it happen, and how to stop it? Communication Research, 47(2), 155–177. https://doi.org/10.1177/0093650219854600
   Web of Science ®Google Scholar
• Yee, J. L., & Niemeier, D. (1996). Advantages and disadvantages: Longitudinal vs. Repeated cross-section surveys (dot:13793). https://rosap.ntl.bts.gov/view/dot/13793
   Google Scholar
• You, K. (2021). NetworkDistance: Distance measures for networks [Computer software]. https://CRAN.R-project.org/package=NetworkDistance
   Google Scholar