Personality as a metric for topic models on social networks

Abstract

As user-generated content suffers from severe problems of data sparseness, many topic models designed for social network are proposed. Without a unified metric, the methods to weigh topics are mixed. Since topic models like Dirichlet Allocation (LDA) can summarize the main information of news articles, topics of short messages on social networking websites should reveal key features of the authors of messages. Personality is the natural characteristic of human. Past work on personality identification has shown that the words people say on social network can reveal people's personality but none of them compare the effects of different topic model tactics on personality identifying. We run LDA and one of its variant (Twitter-LDA) on real social network data (Facebook status messages) then use the topics distribution as features to identify pre-labelled Big-Five personality traits. The results demonstrate that the likelihood of personality as a metric can discover more features of topic models than the model designer said. Furthermore, our research add values to personality identification.