Disperse and Preserve the Perverse: Computing How Hip-Hop Censorship Changed Popular Music Production in China

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How do states censor an artistic genre that challenges the social norms? The answers to this question in the existing literature are mostly based on investigations of particular artists or artworks of the genre, while the impact on the entire genre field remains unchecked. This paper explores how censorship of an artistic genre changes artistic forms of that genre while also triggering strategic reactions from the artists of closely related genres. Using an original dataset of 53,364 songs released on a Chinese online music platform, I study the impact of Hip-Hop censorship in China in 2018 on Hip-Hop as well as Pop, Rock, and Folk songs in terms of how they sound and what topics they write about in the lyrics. I propose a novel approach to measuring sound similarities and topic prevalence in song lyrics using computational tools including Music Information Retrieval (MIR), neural networks, and topic models. I found that Hip-Hop songs produced after the censorship sound significantly different from those before, with a bigger impact on songs that were played frequently on the platform than those that were not. Moreover, genres of similarly restricted production, such as Rock, sound more like Hip-Hop after the censorship, while genres of large-scale production, such as Pop, sound less “Hip-Hopy”. The censorship also made Hip-Hop musicians engage less with topics related to violence, smoking, or drinking but more with sexual content in a covered form. The findings thus suggest a model of dispersion in explaining the mechanism of censorship in cultural production.