Tracking Fluctuations in Psychological States using Social Media Language: A Case Study of Weekly Emotion

Abstract

Personality psychologists are increasingly documenting dynamic, within–person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality–relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text–based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human–annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self–reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model–predicted emotion and self–reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology's evolution into a dynamic science. © 2020 European Association of Personality Psychology