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%).

通过使用出版商元数据以隐含发布者的模板、文本类型、政治立场和可信度，我们提出了一种新颖的验证框架 Style-News，用于防止有害的虚假信息从恶意社交媒体传播，同时通过识别风格与出版商对应以及区分给定新闻的来源是人类撰写还是机器生成，我们训练了一个适应特定出版商生成新闻内容的风格感知神经网络生成器和风格和来源鉴别器。通过整合各种维度指标（语言流畅性、内容保留性和风格遵循性）评估生成内容的质量，我们证明了 Style-News 在流畅性上超过了以前的方法 0.35，内容上高出 15.24，风格上高出 0.38 的幅度。此外，我们的鉴别模型在发布者预测（高达 4.64%）和神经假新闻检测（+6.94%~31.72%）方面优于最先进的基准模型。