Abstract
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.
Highlights
Human societies are characterized by extensive communicative exchanges
Collective belief synchronization is dependent on network structure
Human societies are organized in social networks of interconnected individuals that exchange information
Summary
Human societies are characterized by extensive communicative exchanges. This dynamic information flow has been shown to exert a strong influence on people, impacting their individual memories (Cuc, Koppel, & Hirst, 2007), their beliefs (Vlasceanu & Coman, 2020), and their behaviors (Frankel & Swanson, 2002). We are interested in expanding this work and programmatically exploring how the synchronization of collective beliefs is influenced by a community’s network structure We are studying this influence considering both a time-independent topological mapping typically used in network analysis (Watts & Strogatz, 1998), as well as the temporal sequencing of conversational interactions in networks (Tang, Musolesi, Mascolo, Latora, 2009). To showcase the impact of temporal sequencing of conversations in driving the formation of collective memories, Momennejad, Duker, & Coman (2019) manipulated when conversations occurred between people who bridged network clusters (either early or late during the community’s conversations) They found that early conversations between bridge individuals lead to increased community-wide convergence compared to late conversations between bridge individuals. Coding for belief endorsement in conversations entailed the additional factor of valence, which denoted whether a mentioned statement was endorsed (strongly, moderately, slightly) or refuted (strongly, moderately, slightly) in conversation, by either the participant (listener belief), their partner (speaker belief), or either (joint belief)
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