Predicting personality traits of microblog users

Abstract

Personality can be defined as a set of characteristics which makes a person unique. Psychological theory suggests that people’s behavior is a reflection of personality. Therefore, it is feasible to predict personality through behavior. Conventional personality assessment is performed by self-report inventory. Participants need to fill in a tedious inventory to get their personality scores. In the large-scale investigation, every returned inventory needs manual computation, which costs much manual efforts and cannot be done in real time. In order to avoid these shortages, this research aims to objectively predict the Big-Five personality from the usage records of Sina Microblog. Since its initial launch in December, 2005, Sina Microblog has been the leading microblogging service provider in China. Millions of users upload and download resources via microblogging status everyday. Therefore, by conducting an online user survey of 444 active users, this paper analyzes the relation modes between personality and online behavior. Furthermore, this research proposes multi-task regression and incremental regression to predict the BigFive personality from online behaviors. The results indicate that correlation factors are significant between different personality dimensions. Besides, our training data set is reliable enough and multi-task regression performs better than other modeling algorithms.