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Tuesday, February 1, 2022

Network Structure Impacts the Synchronization of Collective Beliefs

Vlasceanu, M., Morais, M. J., & Coman, A. 
(2021). Journal of Cognition and Culture.


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.

From the Discussion

Understanding the mechanisms by which collective beliefs take shape and change over time is essential from a theoretical perspective (Vlasceanu, Enz, Coman, 2018), but perhaps even more urgent from an applied point of view.  This urgency is fueled by recent findings showing that false news diffuse farther, faster, deeper, and more broadly than true ones in social networks (Vosoughi, Roy, Aral, 2018), and that news can determine what people discuss and even change their beliefs (King, Schneer, White, 2017). And given that beliefs influence people’s behaviors (Shariff & Rhemtulla, 2012; Mangels, Butterfield, Lamb, Good, Dweck, 2006; Ajzen, 1991; Hochbaum, 1958), understanding the dynamics of collective belief formation is of vital social importance as they have the potential to affect some of the most impending threats our society is facing from pandemics (Pennycook, McPhetres, Zhang, Rand, 2020) to climate change (Benegal & Scruggs, 2018). Thus, policy makers could use such findings in designing misinformation reduction campaigns targeting communities (Dovidio & Esses, 2007; Lewandowsky et al., 2012). For instance, these findings suggest such campaigns be sensitive of the conversational network structures of their targeted communities. Knowing how members of these communities are connected, and leveraging the finding that people synchronize their beliefs mainly with individuals they are directly connected to, could inform intervention designers how communities with different connectivity structures might respond to their efforts. For example, when targeting a highly interconnected group, intervention designers could expect that administering the intervention to a few well-connected individuals will have a strong impact at the community level. In contrast, when targeting a less interconnected group, intervention designers could administer the intervention to more central individuals for a comparable effect.