On Predicting Sociodemographic Traits and Emotions from Communications in Social Networks and Their Implications to Online Self-Disclosure.

Abstract

Social media services such as Twitter and Facebook are virtual environments where people express their thoughts, emotions, and opinions and where they reveal themselves to their peers. We analyze a sample of 123,000 Twitter users and 25 million of their tweets to investigate the relation between the opinions and emotions that users express and their predicted psychodemographic traits. We show that the emotions that we express on online social networks reveal deep insights about ourselves. Our methodology is based on building machine learning models for inferring coarse-grained emotions and psychodemographic profiles from user-generated content. We examine several user attributes, including gender, income, political views, age, education, optimism, and life satisfaction. We correlate these predicted demographics with the emotional profiles emanating from user tweets, as captured by Ekman's emotion classification. We find that some users tend to express significantly more joy and significantly less sadness in their tweets, such as those predicted to be in a relationship, with children, or with a higher than average annual income or educational level. Users predicted to be women tend to be more opinionated, whereas those predicted to be men tend to be more neutral. Finally, users predicted to be younger and liberal tend to project more negative opinions and emotions. We discuss the implications of our findings to online privacy concerns and self-disclosure behavior.