Generative adversarial networks for bias flipping

Abstract

The disinformation news in media channels such as social media websites or online newspapers has become a big challenge for many organizations, governments, and scientific researchers. In connection to fake news, the political bias (left-wing or right-wing) of the news articles are recently receiving more attention. In this thesis, we leverage the Adversarially Regularized AutoEncoder (ARAE) model, which enhances the adversarial autoencoder (AAE) by learning a parameterized prior as a Generative Adversarial Networks (GAN) to generate bias-flipped headlines. We perform the experiments with multiple datasets then discuss how these approaches contribute to the bias flipping and detecting problems.