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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-07-05
摘要
文化 演化 is driven by how we choose what to consume and share with others. A common belief is that the 文化 artifacts that succeed are the ones that balance novelty and conventionality. This "balance theory" suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction, a unique data source that mitigates many common critical shortcomings in the study of creative works. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the b
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