Misinformation-oriented expert finding in social networks

Abstract

Due to the distributed and decentralized nature of social media, respective content that contains misinformation is usually propagated without any type of moderation, which may mislead the public and have a profound real-world impact. In addition, it is quite challenging to distinguish misinformation with high precision, since the content is often short and lacks of semantics. A promising solution is to utilize the crowdsourcing wisdom that pushes the suspected misinformation to relevant users based on the expertise and collects the assessments to judge the credibility. Even though a lot of expert finding models have been employed, however, these methods cannot effectively deal with the misinformation-oriented expert matching tasks since the data collected from social network is different form traditional text collection. To this end, we focus on how to obtain an appropriate matching between the suspect misinformation and corresponding experts, and propose a multi-topic expert finding method, called LTM (List based Topic Model), to sufficiently utilize crowdsourcing wisdom. Moreover, we optimize the query results with the help of supervised information that extracted from Twitter Lists. Finally, we demonstrate the effectiveness of our work with experiments on real-world data and verify the superiority of our proposed model in accuracy.