Predicting Personality from Book Preferences with User-Generated Content Labels

Psychological studies have shown that personality traits are associated with book preferences. However, past findings based on questionnaires are limited to conventional book genres and do not capture niche content (e.g., family drama) and reading behaviors (e.g., backburners). For a more comprehensive measure of book content, this study harnesses a massive archive of content labels, also known as ‘tags’, created by users of a book review website, Goodreads.com. Combined with data on preferences and personality scores collected from Facebook users, the tag labels achieve high accuracy in personality prediction by psychological standards. Additionally, we group tags into broader genres to check their validity against past findings. Our results are robust across both tag-level and genre-level analyses and are consistent with existing literature. Moreover, user-generated tag labels reveal unexpected insights, such as cultural differences, book reading behaviors, and other non-content factors affecting preferences. To our knowledge, this is currently the largest study that explores the relationship between personality and book content preferences.