标题： 跟踪可信和可疑网站上的英语新闻叙述的采用和轨迹 显示英文标题

标题： Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites

摘要： 理解误导性和明显虚假的信息如何进入新闻生态系统仍然是一个困难的挑战，这需要追踪叙事如何在数千个边缘和主流新闻网站之间传播。 为了做到这一点，我们引入了一个系统，该系统利用基于编码器的大语言模型和零样本立场检测，可扩展地识别和追踪超过4000个事实不可靠、混合可靠性以及事实可靠的英文新闻网站上的新闻叙事及其态度。 在18个月的时间内运行我们的系统，我们追踪了146,000条新闻故事。 通过基于网络的干扰（NETINF算法），我们表明新闻叙事的路径以及网站对特定实体的立场可以用来揭示倾斜的宣传网络（例如，反疫苗和反乌克兰）并确定在更广泛的新闻生态系统中传播这些态度最具影响力的网站。 我们希望增加对我们分布式新闻生态系统的可见性，有助于报道和核实宣传和虚假信息。 显示英文摘要

摘要： Understanding how misleading and outright false information enters news ecosystems remains a difficult challenge that requires tracking how narratives spread across thousands of fringe and mainstream news websites. To do this, we introduce a system that utilizes encoder-based large language models and zero-shot stance detection to scalably identify and track news narratives and their attitudes across over 4,000 factually unreliable, mixed-reliability, and factually reliable English-language news websites. Running our system over an 18 month period, we track the spread of 146K news stories. Using network-based interference via the NETINF algorithm, we show that the paths of news narratives and the stances of websites toward particular entities can be used to uncover slanted propaganda networks (e.g., anti-vaccine and anti-Ukraine) and to identify the most influential websites in spreading these attitudes in the broader news ecosystem. We hope that increased visibility into our distributed news ecosystem can help with the reporting and fact-checking of propaganda and disinformation.