Style-News: Incorporating Stylized News Generation and Adversarial
  Verification for Neural Fake News Detection
Wei-Yao Wang Yu-Chieh Chang Wen-Chih Peng
With the improvements in generative models, the issues of producing
hallucinations in various domains (e.g., law, writing) have been brought to
people's attention due to concerns about misinformation. In this paper, we
focus on neural fake news, which refers to content generated by neural networks
aiming to mimic the style of real news to deceive people. To prevent harmful
disinformation spreading fallaciously from malicious social media (e.g.,
content farms), we propose a novel verification framework, Style-News, using
publisher metadata to imply a publisher's template with the corresponding text
types, political stance, and credibility. Based on threat modeling aspects, a
style-aware neural news generator is introduced as an adversary for generating
news content conditioning for a specific publisher, and style and source
discriminators are trained to defend against this attack by identifying which
publisher the style corresponds with, and discriminating whether the source of
the given news is human-written or machine-generated. To evaluate the quality
of the generated content, we integrate various dimensional metrics (language
fluency, content preservation, and style adherence) and demonstrate that
Style-News significantly outperforms the previous approaches by a margin of
0.35 for fluency, 15.24 for content, and 0.38 for style at most. Moreover, our
discriminative model outperforms state-of-the-art baselines in terms of
publisher prediction (up to 4.64%) and neural fake news detection (+6.94%
$\sim$ 31.72%).