From Retweet to Believability: Utilizing Trust to Identify Rumor Spreaders on Twitter

Abstract

Ubiquitous use of social media such as microblogging platforms brings about ample opportunities for the false information to diffuse online. It is very important not just to determine the veracity of information but also the authenticity of the users who spread the information, especially in time-critical situations like real-world emergencies, where urgent measures have to be taken for stopping the spread of fake information. In this work, we propose a novel machine learning based approach for automatic identification of the users spreading rumorous information by leveraging the concept of believability, i.e., the extent to which the propagated information is likely to be perceived as truthful, based on the trust measures of users in Twitter's retweet network. We hypothesize that the believability between two users is proportional to the trustingness of the retweeter and the trustworthiness of the tweeter, which are two complementary measures of user trust and can be inferred from retweeting behaviors using a variant of HITS algorithm. With the retweet network edge-weighted by believability scores, we use network representation learning to generate user embeddings, which are then leveraged to classify users into as rumor spreaders or not. Based on experiments on a very large real-world rumor dataset collected from Twitter, we demonstrate that our method can effectively identify rumor spreaders and outperform four strong baselines with large margin.