Predicting Personality on Social Media with Semi-supervised Learning

Abstract

Personality research on social media is a hot topic recently due to the rapid development of social media as well as the central importance of personality study in psychology, but most studies are conducted on inadequate label samples. Our research aims to explore the usage of unlabeled samples to improve the prediction accuracy. By conducting n user study with 1792 users, we adopt local linear semi-supervised regression algorithm to predict the personality traits of Micro blog users. Given a set of Micro blog users' public information (e.g., Number of followers) and a few labeled users, the task is to predict personality of other unlabeled users. The local linear semi-supervised regression algorithm has been employed to establish prediction model in this paper, and the experimental results demonstrate the usage of unlabeled data can improve the accuracy of prediction.