Predicting Personality from Book Preferences with User-Generated Content Labels

Predicting Personality from Book Preferences with User-Generated Content Labels. Ng Annalyn, Maarten W. Bos, Leonid Sigal, and Boyang Li. IEEE Transactions On Affective Computing, 2017. https://arxiv.org/abs/1707.06643

Abstract—Psychological studies have shown that personality traits are associated with book preferences. However, past findings are based on questionnaires focusing on conventional book genres and are unrepresentative of niche content. 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 an online book catalogue, 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. We also group tags into broader genres, to check their validity against past findings. Our results are robust across both tag and genre levels of analyses, and 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.

Index Terms—Personality Profiling, Narrative Preferences, Social Media, Behavioural Footprints