Summary of Style-news: Incorporating Stylized News Generation and Adversarial Verification For Neural Fake News Detection, by Wei-yao Wang et al.
Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection
by Wei-Yao Wang, Yu-Chieh Chang, Wen-Chih Peng
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of generating hallucinations in various domains, such as law or writing, which can lead to misinformation concerns. The focus is on “neural fake news,” where neural networks aim to mimic real news styles to deceive people. To combat this problem, the authors propose a novel verification framework called Style-News, utilizing publisher metadata to imply templates, text types, political stance, and credibility. They also introduce a style-aware neural news generator as an adversary, conditioning news content for a specific publisher. The authors train style and source discriminators to identify publishers and distinguish between human-written or machine-generated sources. The paper evaluates the generated content using dimensional metrics like language fluency, content preservation, and style adherence, demonstrating that Style-News outperforms previous approaches by significant margins. Additionally, their discriminative model excels in publisher prediction (up to 4.64%) and neural fake news detection (+6.94% ∼ 31.72%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about stopping fake news from spreading on social media. Fake news is when someone creates a story that looks real but isn’t, trying to trick people into believing it. The authors want to stop this by creating a special tool called Style-News. It uses information about the publisher of a news article, like what kind of articles they usually write and what their political views are. They also created a fake news generator that tries to mimic real news styles to see if their tool can catch it. The results show that their tool is very good at stopping fake news and identifying who wrote an article. |
Keywords
» Artificial intelligence » Discriminative model
