Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection (2024)

Abstract: 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% 31.72%). We plan to release our Style-News publicly, with the aim of improving neural fake news detection.

## introduction in recent years, social media have been used as platforms for people to share information due to non-distance on the internet. however, the amount of deceptive news has also increased from vicious social media such as content farms by changing some words from their templates; this problem has been widely tackled by detecting the veracity of the news @xcite @xcite . with the advancement of generative pre-trained models (e.g., openai (2023)), the issues of hallucinatory contents have been raised in various domains, e.g., scientific writing @xcite , law @xcite . in this paper, we focus on neural fake news, which has become an emerging societal crisis @xcite @xcite , aiming to produce human-like news via ai models at scale to defraud humans @xcite . therefore, it is crucial to develop verification techniques for defending against neural fake news 1 . the recent progress of neural fake news lies primarily in synthetic news generation. early research on synthetic news generation relied on hand-written rules (van der kaa and krahmer, 2014) or templates @xcite . with the proposed controllable text generation (ctg), text generation can be applied based on given attributes @xcite . grover @xcite produces ctg on multi-field documents to create synthetic news, including domain, date, authors, headline, and body. however, grover neglects inherent factual discrepancies, which are tackled by retrieving external facts to enhance output consistency @xcite . despite the above progress, there are two limitations in the previous work. first, existing approaches to neural fake news detection fail to contemplate style information 2 . in this paper, we focus on an unexplored facet of the style of news in neural news generation: publisher (e.g., cnn or bbc), which can be adopted as a template for vicious social media (e.g., content farms) to produce fake news that can attract readers to read news from the corresponding publisher (baptista and gradim, 2020). for example, news content, political stance, and social engagements will be influenced by hyper-partisan publishers. furthermore, different publishers are likely to describe an event with dissimilar content. as shown in figure 1 , we can observe that two publishers used different titles to describe the afghanistan earthquake. the former states the event with the format highlight: overview event, whereas the latter uses a declarative sentence. these can be viewed as templates from specific publishers, where malicious groups are able to produce fake news based on the templates to deceive readers who often read specific news. therefore, it becomes important to consider publisher information in synthetic neural news to detect it accurately before it is widely spread. second, previous work (e.g., @xcite ; @xcite ) evaluates the performance of defending neural fake news to classify the source of their generated news and the real news. we argue that the discriminators are trained to distinguish generated text from the corresponding generators, which makes the evaluation process unfair due to the fitting discriminators. it is essential to evaluate additional synthetic news that is not seen by models for fair comparisons. in this work, we propose a novel framework, style-news, with stylized news generation and two discriminators for publisher and neural fake news detection. stylized news generation (sng) is introduced to utilize publisher information as an explicit style for controllably generating human-like news content. to achieve fine-grained performance for sng, the style discriminator is designed as a verifier for predicting the publisher of the generated content. in addition, neural fake news detection (nfnd) is proposed to enhance the accuracy of distinguishing human-written and machine-generated news, which can be viewed as a news verifier. to tackle the second issue, we utilize the public dataset consisting of both synthetic and real news, voa-kg2txt @xcite , which is generated by a separate model, to fairly verify the capability and robustness of our neural fake news detection and other baselines. the contributions of this paper are summarized as follows: meanwhile, the stylized news generation incorporates publisher information to produce style-adherence and human-like news content. most of the research on stylized text generation puts efforts into the psycholinguistic aspect such as formal and casual with supervised settings @xcite . however, supervised training requires a large amount of labeled data, which is difficult to generalize to practical tasks. @xcite tackled this issue by integrating a plm with attribute classifiers to construct controlled text generation without training on the language model. stylelm @xcite pre-trains a transformer-based masked language model and fine-tunes on an author-specific corpus using dae loss. however, the style of information has not been addressed in the neural news generation, which produces human-like news efficiently to deceive people based on existing publisher templates. neural fake news detection. the issues of fake news detection have been widely discussed since fake news covers a wide range of topics that may influence the public's views, political motives as well as social engagements @xcite . with the improvement of the generative plm, @xcite identified the problems of neural fake news and developed verification techniques by constructing controllable news generation as an adversary, and exploring potential defenses to mitigate the threats. to tackle the limitations of contradiction or missing details between the generated news and input prompt, factgen @xcite and in-fosurgeon @xcite are introduced to improve the consistency of synthetic news by incorporating external knowledge. nonetheless, previous work failed to explore style information to prevent neural fake news with specific templates, while we incorporate publisher information to generate style-aware news content to demonstrate the great potential of using style information and the awareness to defend against misinformation. ## conclusion this paper presents style-news, a novel adversarial framework to defend against the urgent neural fake news problem. distinct from existing generative models, our style-aware generator produces news with text prompts not only from news highlights and content, but also from publisher information, allowing the integration of additional metadata in the realm of text-metadata compositions. meanwhile, our neural fake news detection captures syntactic information by constructing the input as a graph for distinguishing the human-like news content. style-news sets new state-of-the-art results on both neural news generation and detection benchmarks with our comprehensive metrics. we believe style-news serves as a flexible framework for neural fake news detection, and multiple interesting directions could be further explored within the framework, such as prompt design, few-shot examples, etc.