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Abstract In a high‐risk environment, such as during an epidemic, people are exposed to a large amount of information, both accurate and inaccurate. Following exposure, they typically discuss the information with each other. Here, we assess the effects of such conversations on beliefs. A sample of 126 M‐Turk participants rated the accuracy of a set of COVID‐19 statements, including accurate information, inaccurate information, and conspiracy theories (pre‐test). They were then paired and asked to discuss these statements (low epistemic condition) or to discuss only the statements they thought were accurate (high epistemic condition). Finally, they rated the accuracy of the initial statements again (post‐test). We do not find an effect of the epistemic condition on belief change. However, we find that individuals are sensitive to their conversational partners and change their beliefs according to their partners' conveyed beliefs. In exploratory analyses, we report predictors of believing COVID‐19 conspiracies.
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Network Structure Impacts the Synchronization of Collective Beliefs
Abstract People’s beliefs are influenced by interactions within their communities. The propagation of this influence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants first rated the accuracy of a set of statements (pre-test) and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following pre-determined network structures (clustered/non-clustered). Finally, they rated the accuracy of the statements again (post-test). The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is influenced by the community’s conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non-clustered networks. We also find that conversational content predicts belief change from pre-test to post-test.
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- Award ID(s):
- 2027225
- PAR ID:
- 10344593
- Date Published:
- Journal Name:
- Journal of Cognition and Culture
- Volume:
- 21
- Issue:
- 5
- ISSN:
- 1567-7095
- Page Range / eLocation ID:
- 431 to 448
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1163/15685373-12340120
