Cycle mapping with adversarial event classification network for fake news detection

In recent years, there is a increase in researchers' interest on social evidence, particularly for fake news detection (FND). However, news posts on social media often include diverse modalities, e.g., text, image, etc., and diverse events related to politics, economics, etc., resulting in significant modality and event differences. How to jointly learn modal-invariant and event-invariant discriminative features effectively of news posts remains a challenge. This paper proposes a novel FND approach, called Cycle Mapping and Adversarial Event Classification Network (CMAECN). It consists of two parts: a multi-modal cycle feature mapping module (CMM) and an adversarial event classification module (AECM). In order to fully reduce modality difference, the CMM module is designed, which performs cross-modal generation between image and text modalities by using the generative model, conducts feature source identification between initial and generated features for each modality with the discriminative model, and reconstructs text or image features with the cross-modal fused features to avoid information loss with the reconstructor. In order to fully reduce event difference, the AECM module is designed to perform event adversarial classification between the event classification task and the event-independent classification task with a multi-task event classifier, where each dimension of the classifier output corresponds to a certain event category, and an additional dimension of the output represents the event-independent category. The network training of CMAECN is conducted by adopting an adversarial scheme. Comprehensive experiments are conducted on two public datasets, and CMAECN shows superior performance compared to the state-of-the-art multi-modal FND methods.