使用用户生成的内容标签从书籍偏好预测个性
Predicting Personality from Book Preferences with User-Generated Content Labels
摘要：
心理学研究表明，性格特征与书籍偏好有关。然而，过去的调查结果是基于传统书籍类型的问卷调查，并不代表利基内容。为了更全面地衡量图书内容，这项研究利用了大量的内容标签档案，也称为“标签”，由在线图书目录Goodreads.com的用户创建。结合从Facebook用户收集的偏好和个性得分数据，标签标签在心理学标准的个性预测中实现了高准确性。我们还将标签分为更广泛的类型，以检查它们对过去发现的有效性。我们的结果是强大的标签和体裁水平的分析，并与现有的文献。此外，用户生成的标签揭示了意想不到的见解，如文化差异，书籍阅读行为和其他影响偏好的非内容因素。据我们所知，这是目前最大的研究，探讨个性和书籍内容偏好之间的关系。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
