Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
This work addresses the challenge of personality assessment for large-scale social media analysis, offering an incremental improvement over existing models.
The researchers tackled the problem of deriving human traits from social media language at scale, finding that their language-based trait construct often predicts non-questionnaire outcomes like income and IQ better than traditional five-factor models while maintaining similar stability.
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 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.