Cross-Domain Fake News Detection Based on Coarse-Fine Grained Environments Reflecting Public Expectation.

Prevalence of fake news has seriously disrupted the information ecosystem and undermined social stability and public trust. It stimulated the development of automatic fake news detection method to tackle this dilemma. Most of the methods can be divided into two types, content-based and propagation-based methods. However, these methods overlook the historical background information of the news events contained in the target news in varying degrees. The environment constructed by the historical context of events related to a news event can reflect the direction of the public’s expectations of the current event with respect to future developments. The expectation can be used to study the direction of fake news that has not been fully explored. Therefore, considering the influence of public expectation, we propose a general fake news detection method based on cross-domain coarse-fine grained environments referred to as CFGE including three parts. Specifically, we first construct a cross-domain coarse-fine grained environment with the news related to the domains which were contained in the target news. Then the coarse-grained embedding and the fine-grained embedding of cross-domain environment are extracted respectively by the proposed environment information capture modules. Finally, based on the gate fusion method, coarse-grained embedding and fine-grained embedding are fused to predict fake news. Extensive experimentation substantiates the superiority of CFGE compared to alternative models, further affirming the efficacy of coarse-fine grained environments.