Latent human traits in the language of social media: An open-vocabulary approach

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

Digital Footprints: Envisaging and Analysing Online Behaviour

Our long-term research goal is the development of complex (and adaptive) behavioural modelling and profiling using a multitude of online datasets; in this paper we look at suitable tools for use in big social data, specifically here on how to ‘envisage’ this complex information. We present a novel way of representing personality traits (using the Five Factor model) with behavioural features (fantasy and profanity). We also present some preliminary ideas around developing a scalable solution to modelling behaviour using swear words