Understanding Book Popularity on Goodreads
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Association for Computing Machinery
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Goodreads has launched the Readers Choice Awards since 2009 where users are able to nominate/vote books of their choice, released in the given year. In this work, we question if the number of votes that a book would receive (aka the popularity of the book) can be predicted based on the characteristics of various entities on Goodreads. We are successful in predicting the popularity of the books with high prediction accuracy (correlation coefficient ~0.61) and low RMSE (~1.25). User engagement and author's prestige are found to be crucial factors for book popularity.
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goodreads, book popularity, prediction
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Number of citations to item: 4
- Sanghyub John Lee, Rouxelle de Villiers (2024): Unveiling Emotional Intensity in Online Reviews: Adopting Advanced Machine Learning Techniques, In: Australasian Marketing Journal 1(33), doi:10.1177/14413582241244808
- Massimo Salgaro (2022): Literary value in the era of big data. Operationalizing critical distance in professional and non-professional reviews, In: Journal of Cultural Analytics 2(7), doi:10.22148/001c.36446
- Maria Antoniak, Melanie Walsh, David Mimno (2021): Tags, Borders, and Catalogs, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(5), doi:10.1145/3449103
- Ryo Shiozaki (2024): Preservation Preferences for Books and Websites as Cultural Heritage: A Conjoint Analysis, In: Collection Management 4(49), doi:10.1080/01462679.2024.2422600