This study tested two preliminary hypotheses about beliefs in narratives about COVID-19. We had hypothesized that individuals would be separable into distinct latent classes based on belief in various narratives about COVID-19, and the LPA analysis identified four statistically and conceptually different subgroups. Further, we speculated that trust in science was lower among that groups that reported high believability for misinformation about COVID-19, which was partially supported by our results. These results should be interpreted as supporting the plausibility of these explanations, but as always, should be replicated and further investigated before definitive conclusions are made. We specifically encourage further replication and extensions of this work and support open dialogue about the findings and their implications.
Profiles of COVID-19 belief subgroups
Prior research on conspiracy theories has suggested that many people in the US believe in at least one conspiracy theory [
12], and that those who do may believe in multiple conspiracy theories [
13]. Our LPA analysis, which included believability not only of conspiracy theories/misinformation, but also of the current scientifically-accepted zoonotic explanation for COVID-19, affirmed this finding and added considerable detail.
Profile 1 reported the lowest believability for each misinformed narrative and reported high believability of the zoonotic narrative. This may suggest that people who are skeptical of misinformation tend to believe the scientifically accepted narrative. Interestingly, however, the converse was not true. In fact, the highest believability in the zoonotic explanation was observed for Profile 2, which reported the highest believability for all explanations. Further, Profile 4 was fairly similar to Profile 2, except for lower endorsement of the 5G theory, which we subjectively note is the least plausible theory on its face, given a complete lack of scientific evidence that wireless technology can transmit a virus. Finally, Profile 3 reported low to moderate believability for all narrative statements but reported the lowest endorsement for the zoonotic explanation. This is also important to note, as it suggests that a generally neutral position on the believability of misinformed narratives does not necessarily translate to endorsement of a scientifically-accepted narrative.
Our data support the existence of multiple and distinct belief profiles for COVID-19 misinformation. Based on these findings, we speculate that one reason providing factual information has not always reduced endorsement of misinformation [
41] is that latent groups of people exist for whom belief in a scientifically-accepted explanation is not a mutually exclusive alternative to belief in misinformation (e.g., Profiles 2 and 4). For people belonging to these subgroups, convincing them of the validity of the scientifically-accepted explanation may simply increase their belief in that explanation, without concomitant reductions in belief in alternative narratives. In addition, it is important to note that even Profile 1, which was the most skeptical of misinformation and which expressed high believability for the zoonotic explanation, reported a mean believability value > 2 for two alternative narratives (laboratory development and liberty restriction). Though such narratives are not strongly supported by currently-available evidence, neither are they scientifically impossible (as is the 5G theory). The liberty restriction narrative, in particular, is multifaceted. While evidence continues to accumulate that COVID-19 is a more serious health threat than influenza (e.g., US Centers for Disease Control and Prevention provisional death counts [
66]), there may still be disagreement about the appropriate public health response. For example, even given the evidence for substantial and positive outcomes from mask-wearing requirements [
38], their implementation continues to be contentious. Thus, in some ways, failure to reject all alternative narratives with complete certainty better reflects true scientific work better than would absolute rejection of all alternative narratives [
40], because they may reflect complex and interlinked systems of beliefs.
Predictors of COVID-19 belief subgroups
In our multinomial logistic regression models, controlling for race/ethnicity, gender, age, and education level (as well as the other predictor variables), political orientation was not significantly associated with belonging to any particular COVID-19 belief subgroup. This finding is consistent with some prior hypotheses [
12], but it is important to reiterate, given the tenor of current political discussion in the US. This is not to say that a bivariate or multivariate association between belief in misinformation and political orientation cannot be identified [
67], but it is to suggest the possibility that trust in science may be an underlying variable driving this differentiation.
Although religious commitment was significantly associated with being part of Profile 3 versus Profile 1, the magnitude of this association was not particularly large in comparison to the findings related to trust in science. In addition, examining the confidence intervals independently of significance levels, one might reasonably speculate that belonging to any of Profiles 2 through 4 might be potentially associated with increased religious commitment. It may be the case that the trust in science variable captures some of the complexity that has been observed in associating religion and belief in misinformation [
22].
Finally, low trust in science was substantially and significantly predictive of belonging to Profiles 2, 3, and 4, relative to Profile 1. However, those profiles were distinguished from Profile 1 not by their failure to believe in the zoonotic explanation, but by their endorsement of alternate explanations. In other words, trusting science and scientists appears to be associated with lower likelihood of expressing a belief pattern that endorses narratives that are definitively, or likely to be, misinformed. In this sense, trust in science was conceptually less related to what narrative to believe, and more related to what narrative(s) are more appropriate to disbelieve.
It is important, on a surface level, to understand the potential importance that trust in science has in understanding how people perceive competing narrative explanations about a major event like the COVID-19 pandemic. Unlike political orientation and religious commitment, which can become part of a personal identity (and hence may be more difficult to modify), trust in science is, on its face, a potentially modifiable characteristic. From a public health standpoint, the strength of the association between trust in science and misinformation believability profiles, combined with the potential mutability of the ‘trust in science’ variable, may indicate a potential opportunity for a misinformation intervention. However, the solution is not likely to be as simply as “just asserting that science can be trusted.” First, consider the conflict described earlier in this manuscript, where there is an inherent tension between conspiratorial thinking and trusting expert opinion. If it were true, for example, that 5G networks were being used to spread COVID-19, then the authorities doing so, and desiring to hide it, would have an interest in debunking the 5G narrative. If “science” and “authority” or “government bodies” become conflated, then lower trust in science may result from distrust of authority, thereby affecting believability of explanations [
68]. Thus, one important consideration might be the importance of working to ensure that science remains non-partisan, including careful vigilance for white hat bias (distortion of findings to support the “correct” outcome) [
69].
Second, although as researchers we believe in the power of the scientific approach to uncover knowledge, there have been well-documented cases of scientific misconduct, such as the 1998 Wakefield et al. paper linking vaccines and autism [
70], as well as other concerns about adherence to high-integrity research procedures [
71]. Anomalies or other issues related to research partnerships can occur as well. While this paper was being prepared for submission, a major COVID-19 study on hydroxychloroquine was retracted due to issues with data access for replication [
72]. At the same time, as researchers, we understand that a single study does not constitute consensus, and that not all methods and approaches yield the same quality of evidence. Science, as a field, scrutinizes itself and tends to be self-correcting – though not always as rapidly as one might wish, and systems regularly have been reconfigured to ensure integrity [
73]. In the time between submission and revision of this paper following peer review, randomized, controlled trials of hydroxychloroquine have been published and have served to disambiguate its clinical utility for COVID-19 (e.g., the RECOVERY trial) [
74]. In this case, the scientific approach appears to have functioned as intended – over time. However, to a person not embedded within the scientific research infrastructure, it is not necessarily irrational to report a lower level of trust in science on the basis of the idea that certain scientific theories have been wrong, study findings do not always agree, and in rare cases, findings have been fraudulently obtained.
Given that trust in science and scientists was the most meaningful factor predicting profile membership, accounting for a wide variety of potential covariates, systematically building trust in science and scientists might be an effective way to inoculate populations against misinformation related to COVID-19, and potentially other misinformation. Based on this study’s findings, this would specifically not take the form of repeatedly articulating factual explanations (especially within a scientific echo chamber [
43]), as this might potentially increase believability of accurate narratives, but only as one among other equally believable narratives. Rather, to improve trust in science, we might consider demonstrating – honestly and openly – how science works, and then articulating why it can be trusted [
40]. Parallel processes such as implementing recommendations to facilitate open science [
75] may also have the secondary effect of improving overall public trust in science. Individuals who both understand [
20,
21] and trust science [
7,
45] appear to be most likely to reject explanations with less supporting evidence while accepting narratives with more supporting evidence.
Limitations
This study has several limitations. First, to conduct rapid research amid a pandemic, we used the mTurk survey platform. As noted in our Methods, this is a widely accepted research platform across multiple disciplines, but it does not produce nationally representative data. Thus, the findings should not be generalized to any specific population without further study. In addition, we suspect, but cannot confirm, that the results would potentially look different outside of the US. Second, because COVID-19 emerged recently, and research on COVID-19 misinformation was initiated even more recently, no validated questionnaires for believability of COVID-19 misinformation existed at the time of survey administration. However, we suggest some face validity for our measures of misinformation believability because the response scale was established in prior research [
52] and because the topics were drawn from a reputable list of misinformed narratives [
23]. Third, as with all inferential models, this study is subject to omitted variable bias [
76], though the magnitude of the association between the latent profiles and the trust in science variable somewhat attenuates this concern. Fourth, since this was a cross-sectional study, we cannot assert any causality or directionality.