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Skip to main content. Sign in. Desktop Navigation. Horizon Behavioral Health We are focused on making sure our members get the right care when you need it. Find an Inspira Provider. What type of care are you looking for? Please select your provider type. Please select your plan. Select Specialty Enter minimum 3 letters for search. Areas of clinical expertise 0 of 6 selected. You can narrow your search to a location.

Need Help? Provider Gender No preference Male Female. Special Needs 0 of 4 Selected. Telemedicine Only Telemedicine practitioners. Plan Specifics. Filter your results Specialty. Gender No preference Male Female. Previous work has shown that there is evidence of echo chambers on real social media platforms around information on vaccines, in general 54 , If such information silos also exist for COVID vaccines, then they may lead to self-selection of misinformation or factual information, inducing individuals to become progressively more or less inclined to vaccinate.

While our study does not directly quantify such social network effects, it emphasizes on the need to do so further. Furthermore, we find correlational evidence that misinformation identified by our participants after exposure as having the most impact on lowering their vaccination intent was made to have a scientific appeal, such as emphasizing on a direct link between a COVID vaccine and adverse effects while using scientific imagery or links to strengthen their claims.

However, our design does not allow causal inferences and we were limited in the type and volume of misinformation presented to respondents. Future research should examine the causal impact of different types of misinformation and identify whether there are other types of misinformation that may be far more impactful on vaccination intent. Therefore, our estimates for the losses in vaccination intent due to misinformation must be placed in the context of this study and the correlational evidence it provides, and caution must be exercised in generalizing these findings to a real-world setting, which may see larger or smaller decreases in vaccination intent depending on the wider context of influencing factors.

Addressing the spread of misinformation will probably be a major component of a successful COVID vaccination campaign, particularly given that misinformation on social media has been shown to spread faster than factually correct information 56 and that, even after a brief exposure, misinformation can result in long-term attitudinal and behavioural shifts 45 , 57 that pro-vaccination messaging may find hard to overcome With regards to COVID, misinformation has even been shown to lead to information avoidance and less systematic processing of COVID information 32 ; however, the amplification of questionable sources of COVID misinformation is highly platform dependent, with some platforms amplifying questionable content less than reliable content This analysis provides a platform to help us test and understand how more effective public health communication strategies could be designed and on whom these strategies would have the most positive impact in countering COVID vaccine misinformation.

Ethical approval for this study was obtained by the London School of Hygiene and Tropical Medicine ethics committee on 15 June with reference A total of 8, respondents recruited via an online panel were surveyed by ORB Gallup International www. Respondent quotas for each country and each group that is both treatment and control were set according to national demographic distributions for gender, age, and sub-national region—the four census regions in the USA 59 and first level of nomenclature of territorial units in the UK Following randomized treatment assignment, 3, UK and 3, US respondents were exposed to images of recently circulating online misinformation related to COVID and vaccines treatment group and 1, respondents in each country were shown images of factual information about a COVID vaccine to serve as a randomized control control group.

All respondents exposed to misinformation were debriefed after the survey; debriefing information can be found in the questionnaire included in Supplementary Information. Some respondent characteristics were recoded to reduce their number and facilitate comparison across the two countries.

To elicit responses that can be most readily interpreted in light of the current state of online misinformation in both the UK and USA, the information shown to respondents—in the form of snippets of social media posts—should satisfy a number of criteria. It should: 1 be recent and relevant to a COVID vaccine; 2 have a high engagement, either through user reach or other publicity, and thus represent information that respondents are not unlikely to be exposed to through social media use; 3 include posts shared by organizations or people with whom respondents are familiar so that, for example, US and UK audiences are not shown information from people with whom they are unfamiliar ; 4 form a distinct set, not replicating content or core messaging, enabling us to probe the most impactful types of misinformation.

To this end, we followed a principled approach to select two sets of five images for the treatment and control groups, respectively, combining both quantitative and qualitative methods. This Boolean search term was based on previous research that used similar search terms obtaining the highest levels of user engagement with COVID media and social media articles containing misinformation. This search string returned over , social media posts that were initially filtered by user engagement and reach to provide the most widely shared and viewed posts.

Two independent coders S. Some posts had relatively low levels of engagement, but were included because they repeatedly appeared in different formats across different outlets and were thus deemed to be influential on social media. Reputable online sources of knowledge were consulted to determine which content was classified as misinformation—that is, information that is regarded false or misleading according to current expert knowledge. Factual information was obtained by a coder S.

Reputable online sources of knowledge were consulted to determine which content is classified as factual information—that is, information that is regarded correct as per current expert knowledge. Information was often from authoritative sources or otherwise referenced to authoritative sources such as vaccine groups and scientific organizations.

We ensured that these five posts were not overtly pro-vaccination and did not reference anti-vaccination campaigns or materials. For instance, information presented included an update on the current state of COVID vaccine trials; the importance of a vaccine to get out of the COVID pandemic; and how a candidate vaccine generates a good immune response.

Supplementary Table 1 presents further details regarding the treatment and control image sets, including detailed explanations for classification of posts as misinformation or factual information. In this study, the outcome of interest, vaccination intent, is measured on a four-level ordered scale.

Using a classical approach in the potential outcomes framework 61 , 62 to determine treatment effects would either necessitate binarizing the outcome—which can lead to loss of information about vaccination intent—or making a strong assumption of linearity of the outcome scale.

Therefore, a hierarchical Bayesian ordered logistic regression framework is used here to estimate the impact of 1 treatment of misinformation on change in vaccination intentv relative to factual information, and 2 how these treatments differentially impact individuals by their sociodemographic characteristics that is, HTEs.

Full model details, including the statistics used to describe all effects, are detailed below. Since vaccination intent is modelled as an ordered variable, one can expect the treatment to impact vaccination intent monotonically. To this end, W is modelled as a monotonic ordered predictor 63 , Using W as a predictor for Y has two advantages: it 1 controls for sampling discrepancies between the treatment and control groups, and 2 allows for the treatment to differentially affect those with different prior vaccination intents.

We use ordered logistic regression 65 to model Y conditional on G , W and covariates X. Then for an individual respondent i we can write:. This modelling framework allows us to model 1 the effect of treatment on vaccination intent and 2 the HTEs through the function f.

We remark that the model for Y specified in equation 2 is equivalent to a traditional linear two-way interaction model for causal estimation under a binary treatment, composed with a logistic sigmoid function to model the cumulative distribution of the ordinal categorical outcome variable However, as Y is an ordered categorical, a conditional expectation has no meaningful interpretation.

Therefore, we can compute a conditional probability mass function, P Y G , and define a corresponding statistic for treatment effect 67 on vaccination intent as:. The interpretation of equations 3 and 4 is as follows. Statistics for measuring HTEs treatment effects may depend on sociodemographic groups: misinformation or factual information may cause some sociodemographic groups to be more or less likely to vaccinate than others. Following the conditional probability mass function framework, these HTEs would correspond to computing the following conditional statistic:.

Because we consider many covariates, in the interest of being concise we cannot estimate conditional treatment effects for every multivariate combination of covariates. However, some progress can be made by considering the following modifications. Firstly, we can compute a different statistic that still permits a form expressed as the linear difference of a function over treatments and controls separately.

In particular, since vaccination intent is ordered, we can define a statistic conveniently in terms of the conditional cumulative distribution function. The larger this statistic is for given y , the less likely the individual x in group g is to have a high vaccination intent. This leads to the following relative measure for heterogeneous effects:. For binary characteristics, we pick the null group as the reference—indicating no trust in a source of COVID information.

The interpretation of the HTE equation 6 is as follows. To study which images, corresponding to misinformation or factual information, are perceived by participants to induce a larger drop in vaccination intent upon exposure, we make use of ratings given by the respondents to each of the 5 images presented along 5 different perception metrics as features to learn how each image metric and each image itself contributes to the measured drop in vaccination intent.

As before, let W denote pre-exposure intent, Y is the post-exposure intent and G is the treatment group. Then, the model definition here is very similar to when pursuing HTEs analysis, except the function of covariates now corresponds to an aggregation of ratings across images and image metrics:. The image metrics considered are, in order, whether 1 the respondents perceived the image to have made them less inclined to vaccinate, 2 they agreed with the image, 3 they found the image trustworthy, 4 they were likely to fact check the information shown in the image, and 5 they were likely to share the image.

The target average proposal acceptance probability for the NUTS sampler was set to 0. The maximum tree depth for the sampler was set to 10 but increased to 15 if the limit was reached for any model. Further information on research design is available in the Nature Research Reporting Summary linked to this article. A copy of the materials used in this study, as displayed to respondents, can be obtained from the authors upon request. Source data are provided with this paper.

Andersen, K. Zhou, P. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature , — World Health Organization. Jackson, L. Mulligan, M. Folegatti, P. Lancet , — Voysey, M. US Food and Drug Administration. European Medicines Agency. Kwok, K. Sanche, S. High contagiousness and rapid spread of severe acute respiratory syndrome coronavirus 2.

Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study. Article Google Scholar. Bellaby, P. BMJ , — Burki, T. Vaccine misinformation and social media. Lancet Digit. Health 1 , E—E Lo, N. Public health and economic consequences of vaccine hesitancy for measles in the United States. JAMA Pediatr.

Megget, K. BMJ , m Geldsetzer, P. Pennycook, G. Fighting COVID misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. The effect of opinion clustering on disease outbreaks. Interface 5 , — McAndrew, S. Mode and frequency of COVID information updates, political values, and future covid vaccine attitudes. Tyson, A. Peretti-Watel, P. Lancet Infect. COVID the deadly threat of misinformation. Altmann, D. Zarocostas, J.

How to fight an infodemic. Lancet , Islam, M. COVID—related infodemic and its impact on public health: a global social media analysis. Kim, H. Effects of COVID misinformation on information seeking, avoidance, and processing: a multicountry comparative study. Kreps, S. Murphy, J. Roozenbeek, J. Open Sci. Romer, D. Ernala, S. How well do people report time spent on Facebook?

In Proc. National Health Service England. Do it for yourself and your friends. Wear a mask to protect you and your friends. Lazer, D. The science of fake news. Science , — Vraga, E. Defining misinformation and understanding its bounded nature: Using expertise and evidence for describing misinformation. Political Commun. Graham, M. Asking about attitude change. Public Opinion Quarterly in the press. Zhu, B. Brief exposure to misinformation can lead to long-term false memories.

Paul, E. Attitudes towards vaccines and intention to vaccinate against COVID implications for public health communications. Lancet Reg. Health Eur. Malik, A. EClinicalMedicine 26 , Callaghan, T. Motta, M. Knowing less but presuming more: Dunning—Kruger effects and the endorsement of anti-vaccine policy attitudes. Bakshy, E. Exposure to ideologically diverse news and opinion on Facebook. Del Vicario, M. The spreading of misinformation online. Natl Acad. USA , — Polarization and fake news: early warning of potential misinformation targets.

ACM Trans. Web 13 , 10 Google Scholar. Schmidt, A. Polarization of the vaccination debate on Facebook. Vaccine 36 , — Cossard, A. Falling into the echo chamber: the Italian vaccination debate on Twitter. Vosoughi, S. The spread of true and false news online.

Pluviano, S. Misinformation lingers in memory: failure of three pro-vaccination strategies. Cinelli, M. US Census Bureau. Census regions and divisions of the United States. Rubin, D. Estimating causal effects of treatments in randomized and nonrandomized studies. Holland, P.

Statistics and causal inference. Modelling monotonic effects of ordinal predictors in Bayesian regression models.


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