Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America.
Melbourne Graduate School of Education, The University of Melbourne, Melbourne, Australia.
PLoS One. 2018 Nov 28;13(11):e0201703. doi: 10.1371/journal.pone.0201703. eCollection 2018.
Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from behavioral data-language use-at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use.
在过去的一个世纪里，人格理论和研究成功地确定了核心特征集，这些特征集始终如一地描述和解释了人们思考、感受和行为方式的基本差异。这些特征是通过理论、字典分析和使用明确的自我报告的调查研究得出的。社交媒体数据的可用性现在使得从大规模的行为数据（语言使用）中自动推导出特征成为可能。我们利用通过 Facebook 获得的语言信息，研究了基于非提示性语言使用来推断一组新的潜在人类特征的过程。我们对这些新特征进行了全面的评估，并将其与流行的五因素人格模型进行了比较。我们发现，我们基于语言的特征构建通常更具通用性，因为它通常比基于问卷的特征（例如某人喜欢的实体、收入和智商）更好地预测非问卷的结果，而这些因素仍然几乎与传统因素一样稳定。我们的方法表明，从日常人类语言使用中得出的人格新构念具有价值。
