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Abstract Why do some songs and musicians become successful while others do not? We show that one of the reasons may be the “first-mover advantage”: artists that stand at the foundation of new music genres tend to be more successful than those who join these genres later on. To test this hypothesis, we have analyzed a massive dataset of over 920,000 songs, including 110 music genres: 10 chosen intentionally and preregistered, and 100 chosen randomly. For this, we collected the data from two music services: Spotify, which provides detailed information about songs’ success (the precise number of times each song was listened to), and Every Noise at Once, which provides detailed genre tags for musicians. 91 genres, out of 110, show the first-mover advantage—clearly suggesting that it is an important mechanism in music success and evolution.

Abstract What are the best methods of capturing thematic similarity between literary texts? Knowing the answer to this question would be useful for automatic clustering of book genres, or any other thematic grouping. This paper compares a variety of algorithms for unsupervised learning of thematic similarities between texts, which we call “computational thematics”. These algorithms belong to three steps of analysis: text pre-processing, extraction of text features, and measuring distances between the lists of features. Each of these steps includes a variety of options. We test all the possible combinations of these options. Every combination of algorithms is given a task to cluster a corpus of books belonging to four pre-tagged genres of fiction. This clustering is then validated against the “ground truth” genre labels. Such comparison of algorithms allows us to learn the best and the worst combinations for computational thematic analysis. To illustrate the difference between the best and the worst methods, we then cluster 5000 random novels from the HathiTrust corpus of fiction.

Detecting the forces behind the success or failure of cultural products, such as books or films, remains a challenge. Three such forces are drift, context-biased selection and selection based on content – when things succeed because of their intrinsic appeal. We propose a tool to study content-biased selection in sets of cultural collections – e.g. libraries or movie collections – based on the ‘shortlist effect’: the fact that smaller collections are more selective and more likely to favour highly appealing items over others. We use a model to show that, when the shortlist effect is at work, content-biased cultural selection is associated with greater nestedness in sets of collections. Having established empirically the existence of the shortlist effect, and of content-biased selection, in 28 sets of movie collections, we show that nestedness contributions can be used to estimate to what extent specific movies owe their success to their intrinsic properties. This method can be used in a wide range of datasets to detect the items that owe their success to their intrinsic appeal, as opposed to ‘hidden gems’ or ‘accidental hits’.

Are there large-scale trends in art history that surpass individual creativity or relatively short artistic movements? Many theories describe art history as a process similar to a change of fashions, while others suggest that art can be progressive – getting better, in some sense, over time. We approach this question anew with the theory of cumulative cultural evolution, which describes cultural accomplishments in terms of innovations that are maintained across generations and accumulated to support ever greater creative potential. In this paper, we empirically test the possibility for cumulative evolution in the techniques used to make an artistic product. Specifically, we measure the size and structure of the production crews in American films in 1910–2010 based on a dataset of 1000 popular films across the century. We find that film crews become exponentially more complex, with a growing set of core jobs, and more innovative in creating new jobs in filmmaking. Our study shows that art history can be cumulative, showing the progressive maintenance of innovative techniques, and thus providing an alternative to the widespread view of art history as a mere fluctuation of trends and fashions.

The paper studies long-term changes in the length of Russian poetry (1750–1921) to reveal the relation of poem length (counted in lines) to a poetic form and its evolution. The research has shown a dramatic decrease in the mean and median poetry lengths during the 19th century. This decrease was followed by the decline in length diversity, which resulted in short poems (8–20 lines) overpopulating the literature during the age of Modernism. We argue that this transformation towards the short form could be understood in the framework of cultural evolution: Russian poetry struggled to keep its literary niche, while being continuously under the pressure of successful large narratives of the 19th century. Therefore, it was forced to develop complexity while being highly constrained formally (accentual-syllabic verse and rhyme maintained for a long time) by the shrunk length of a lyrical poem.