“I was Right about Vaccination”: Confirmation Bias and Health Literacy in Online Health Information Seeking

Meppelink, Corine S.; Smit, Edith G.; Fransen, Marieke L.; Diviani, Nicola

doi:10.1080/10810730.2019.1583701

Cited by 160 publications

(125 citation statements)

References 34 publications

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“…11 Further analysis of AI websites and social media is needed to determine the full extent of these and other biases. 87 Second, the evidence for some of the individual nudge approaches has also been challenged; for example, a failure to replicate some of the key studies of priming, 91 though this does not invalidate the fact that the industry uses approaches based on priming, even if the evidence is flawed. Finally, we cannot be certain that the AI examples that we cite are intentional.…”

Section: Limitations and Strengthsmentioning

confidence: 99%

Dark Nudges and Sludge in Big Alcohol: Behavioral Economics, Cognitive Biases, and Alcohol Industry Corporate Social Responsibility

et al. 2020

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r Nudges steer people toward certain options but also allow them to go their own way. "Dark nudges" aim to change consumer behavior against their best interests. "Sludge" uses cognitive biases to make behavior change more difficult. r We have identified dark nudges and sludge in alcohol industry corporate social responsibility (CSR) materials. These undermine the information on alcohol harms that they disseminate, and may normalize or encourage alcohol consumption. r Policymakers and practitioners should be aware of how dark nudges and sludge are used by the alcohol industry to promote misinformation about alcohol harms to the public. Context: "Nudges" and other behavioral economic approaches exploit common cognitive biases (systematic errors in thought processes) in order to influence behavior and decision-making. Nudges that encourage the consumption of harmful products (for example, by exploiting gamblers' cognitive biases) have been termed "dark nudges." The term "sludge" has also been used to describe strategies that utilize cognitive biases to make behavior change harder. This

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Section: Limitations and Strengthsmentioning

confidence: 99%

Dark Nudges and Sludge in Big Alcohol: Behavioral Economics, Cognitive Biases, and Alcohol Industry Corporate Social Responsibility

et al. 2020

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“…The role of confirmation bias has already been explored in health information sampling research suggesting that it may significantly affect how information is collected. In recent research, Meppelink et al [ 56 ] investigated the role of confirmation bias in information seeking with respect to early-childhood vaccination and found that a priori vaccination beliefs biased selection of online health information—people predominantly selected information that was consistent with their existing beliefs (ie, selective exposure) [ 57 ]. The significant effect of sampling strategy index on causal estimations showed that, in addition, a partial selection of information in which belief-supporting evidence is overrepresented may be related to damaged causal estimation (note that the correlational nature of our design does not allow us to discriminate whether the biased searching caused the estimates to be biased, or whether a stronger initial belief about the causal relationship might have biased the sampling strategy, strengthening the initial belief).…”

Section: Discussionmentioning

confidence: 99%

Biased Sampling and Causal Estimation of Health-Related Information: Laboratory-Based Experimental Research

Moreno-Fernández

Matute

2020

J Med Internet Res

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Background The internet is a relevant source of health-related information. The huge amount of information available on the internet forces users to engage in an active process of information selection. Previous research conducted in the field of experimental psychology showed that information selection itself may promote the development of erroneous beliefs, even if the information collected does not. Objective The aim of this study was to assess the relationship between information searching strategy (ie, which cues are used to guide information retrieval) and causal inferences about health while controlling for the effect of additional information features. Methods We adapted a standard laboratory task that has previously been used in research on contingency learning to mimic an information searching situation. Participants (N=193) were asked to gather information to determine whether a fictitious drug caused an allergic reaction. They collected individual pieces of evidence in order to support or reject the causal relationship between the two events by inspecting individual cases in which the drug was or was not used or in which the allergic reaction appeared or not. Thus, one group (cause group, n=105) was allowed to sample information based on the potential cause, whereas a second group (effect group, n=88) was allowed to sample information based on the effect. Although participants could select which medical records they wanted to check—cases in which the medicine was used or not (in the cause group) or cases in which the effect appeared or not (in the effect group)—they all received similar evidence that indicated the absence of a causal link between the drug and the reaction. After observing 40 cases, they estimated the drug–allergic reaction causal relationship. Results Participants used different strategies for collecting information. In some cases, participants displayed a biased sampling strategy compatible with positive testing, that is, they required a high proportion of evidence in which the drug was administered (in the cause group) or in which the allergic reaction appeared (in the effect group). Biased strategies produced an overrepresentation of certain pieces of evidence at the detriment of the representation of others, which was associated with the accuracy of causal inferences. Thus, how the information was collected (sampling strategy) demonstrated a significant effect on causal inferences (F1,185=32.53, P<.001, η2p=0.15) suggesting that inferences of the causal relationship between events are related to how the information is gathered. Conclusions Mistaken beliefs about health may arise from accurate pieces of information partially because of the way in which information is collected. Patient or person autonomy in gathering health information through the internet, for instance, may contribute to the development of false beliefs from accurate pieces of information because search strategies can be biased.

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“…Social media has a major impact on the way that people interact and exchange information, and this is also the case in the antivaccination debate [ 53 ]. Since information on the internet may influence people’s behavior regarding vaccination, it is important that facts are shared; yet, the social media platforms that enable individual users to find and share information easily can have two side effects on vaccination decision: they can lead people to get vaccinated [ 54 ], but antivaccination movements also can be strengthened [ 55 , 56 ]. Thus, it is relevant to observe the vaccination debate on social media and to examine how antivaccination movements proceed [ 57 ].…”

Section: Related Workmentioning

confidence: 99%

#Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses

Kim

Song

Lee

2020

IJERPH

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A dramatic increase has been registered in the number of social media posts in photo form as well as in hashtag activism. Hashtags, which manifest thoughts and feelings clearly and concisely, originated on Twitter, where the length of a post is limited; their use, however, has expanded into other social media services, including Instagram. Hashtags, which make it easy to find and express support for posts of interest, have been widely used for online activism, although they have been criticized for fostering confirmation bias. Moreover, hashtag activism in photo form has been relatively understudied. This research analyzed Instagram photos with antivaccination hashtags as an example of hashtag activism through photos. In addition, we examined how the photo features were related to public response, which was manifested via engagement and comment sentiment. The results suggest that the photos which were categorized into “text” took the largest share. It was also found that the major way of claiming was to imprint key messages that persuade people not to vaccinate with remarks from professionals on photos and provide a source of supporting information in the post text with hashtags of antivaccine intention. Various photo features showed associations with engagement and comment sentiment, but the directions of correlation were usually the opposite: these results suggest that engagement and comment sentiment are separate domains that reveal different public responses.

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“I was Right about Vaccination”: Confirmation Bias and Health Literacy in Online Health Information Seeking

Cited by 160 publications

References 34 publications

Dark Nudges and Sludge in Big Alcohol: Behavioral Economics, Cognitive Biases, and Alcohol Industry Corporate Social Responsibility

Dark Nudges and Sludge in Big Alcohol: Behavioral Economics, Cognitive Biases, and Alcohol Industry Corporate Social Responsibility

Biased Sampling and Causal Estimation of Health-Related Information: Laboratory-Based Experimental Research

#Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses

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