Assessment of Tweet Credibility with LDA Features

With the fast development of Social Networking Services (SNS) such as Twitter, which enable users to exchange short messages online, people can get information not only from the traditional news media but also from the masses of SNS users. However, SNS users sometimes propagate spurious or misleading information, so an effective way to automatically assess the credibility of information is required. In this paper, we propose methods to assess information credibility on Twitter, methods that utilize the “tweet topic” and “user topic” features derived from the Latent Dirichlet Allocation (LDA) model. We collected two thousand tweets labeled by seven annotators each, and designed effective features for our classifier on the basis of data analysis results. An experiment we conducted showed a 3% improvement in Area Under Curve (AUC) scores compared with existing methods, leading us to conclude that using topical features is an effective way to assess tweet credibility.