Learning and enforcing a cultural consensus in online communities

Necdet Gürkan, Jordan W. Suchow

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

Online communities rely on their members to understand and follow community norms, which they learn by observing others and the consequences of their behavior, seeing codes of conduct, and receiving feedback via moderation. Here, to determine the contribution of each source of learning to the preservation of a social norm, we extend cultural consensus theory, a mathematical framework for identifying the cultural consensus in a community. In particular, we extend the model to include learning from experience, centralized moderation, and decentralized moderation, three features commonly found in online communities. We then apply the extended model to data from an online community dedicated to preserving a norm related to the psychophysical scaling of intersubjective notions of beauty derived from facial aesthetics. We find that users' perceptual alignment with the norm before enculturation predicts involvement in the community and that experience in the community is an important indicator for group perceptual learning.

Original languageEnglish
Pages1152-1159
Number of pages8
StatePublished - 2022
Event44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canada
Duration: 27 Jul 202230 Jul 2022

Conference

Conference44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022
Country/TerritoryCanada
CityToronto
Period27/07/2230/07/22

Keywords

  • Bayesian modeling
  • cultural consensus
  • face perception
  • online communities

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