ICE: Information Credibility Evaluation on Social Media via Representation Learning

With the rapid growth of social media, rumors are also spreading widely on social media, such as microblog, and bring negative effects to human life. Nowadays, information credibility evaluation has drawn attention from academic and industrial communities. Current methods mainly focus on feature engineering and achieve some success. However, feature engineering based methods often require a lot of labor and cannot fully reveal the underlying relations among data. In our viewpoint, the key elements of evaluating credibility are concluded as who, what, when, and how. These existing methods cannot well model the correlation among these key elements during the spreading of microblogs. In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media. In ICE, latent representations are learnt for modeling who, what, when, and how, and these key elements means user credibility, behavior types, temporal properties, and comment attitudes respectively. The aggregation of these factors in the microblog spreading process yields the representation of a user’s behavior, and the aggregation of these dynamic representations generates the credibility representation of an event spreading on social media. Besides, in ICE, a pairwise learning method is applied to maximize the credibility difference between rumors and nonrumors. To evaluate ICE, we conduct a series of experiments on a Sina Weibo dataset, and the experimental results show that the proposed ICE model outperforms the state-of-the-art methods.