With the increasing popularity of generative large language models (LLMs)
like ChatGPT, an increasing number of news websites have begun utilizing them
to generate articles. However, not only can these language models produce
factually inaccurate articles on reputable websites but disreputable news sites
can utilize these LLMs to mass produce misinformation. To begin to understand
this phenomenon, we present one of the first large-scale studies of the
prevalence of synthetic articles within online news media. To do this, we train
a DeBERTa-based synthetic news detector and classify over 12.91 million
articles from 3,074 misinformation and mainstream news websites. We find that
between January 1, 2022 and April 1, 2023, the relative number of synthetic
news articles increased by 79.4% on mainstream websites while increasing by
342% on misinformation sites. Analyzing the impact of the release of ChatGPT
using an interrupted-time-series, we show that while its release resulted in a
marked increase in synthetic articles on small sites as well as misinformation
news websites, there was not a corresponding increase on large mainstream news
websites. Finally, using data from the social media platform Reddit, we find
that social media users interacted more with synthetic articles in March 2023
relative to January 2022.

研究发现，随着大型语言模型（LLM）的普及，越来越多的新闻网站开始利用它们生成文章，导致合法网站的 factual 准确性不断下降，不法新闻网站可以利用这些 LLM 大量制造虚假信息。通过对 3074 家不良媒体和主流新闻网站的 12.91 百万篇文章进行分类与研究，发现在 2022 年 1 月 1 日至 2023 年 4 月 1 日期间，主流网站的合成新闻文章相对数量增加了 79.4％。而不良网站的增幅则高达 342％，同时还有所关注的是，ChatGPT 发布后，小型网站和不良媒体的合成文章数量急剧增加，但在大型主流新闻网站上并没有相应的增长。最后，社交媒体 Reddit 中的数据显示，社交媒体用户在 2023 年 3 月比 2022 年 1 月与合成文章的互动更多。

机器制造的媒体：监测虚假信息和主流新闻网站上机器生成文章的动员

Machine-Made Media: Monitoring the Mobilization of Machine-Generated  Articles on Misinformation and Mainstream News Websites

The advanced text generation methods have witnessed great success in text
summarization, language translation, and synthetic news generation. However,
these techniques can be abused to generate disinformation and fake news. To
better understand the potential threats of synthetic news, we develop a new
generation method FactGen to generate high-quality news content. The existing
text generation methods either afford limited supplementary information or lose
consistency between the input and output which makes the synthetic news less
trustworthy. To address these issues, FactGen retrieves external facts to
enrich the output and reconstructs the input claim from the generated content
to improve the consistency among the input and the output. Experiment results
on real-world datasets show that the generated news contents of FactGen are
consistent and contain rich facts. We also discuss the possible defending
method to identify these synthetic news pieces if FactGen is used to generate
synthetic news.

通过使用扩展信息和重构输入内容，我们提出了新的文章生成方法 FactGen 来提高生成新闻文本的一致性和内容丰富性，并讨论了鉴别这些合成新闻的可能方法。