DistrustRank

Abstract

In this paper we propose a semi-supervised learning strategy to automatically separate fake News from reliable News sources: DistrustRank. We first select a small set of unreliable News, manually evaluated and classified by experts on fact checking portals. Once this set is created, DistrustRank constructs a weighted graph where nodes represent websites, connected by edges based on a minimum similarity between a pair of websites. Next it computes the centrality using a biased PageRank, where a bias is applied to the selected set of seeds. As an output of the proposed model we obtain a trust (or distrust) rank that can be used in two ways: a) as a counter-bias to be applied when News about a specific subject is ranked, in order to discount possible boosts achieved by false claims; and b) to assist humans to identify sources that are likely to be source of fake News (or that are likely to be reputable), suggesting websites that should be examined more closely or to be avoided. In our experiments, DistrustRank outperforms the supervised approaches in either ranking and classification task.