Learning and enforcing a cultural consensus in online communities

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