追踪可信与可疑网站中英语新闻叙事的呈现与传播轨迹
Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites
AI总结：
本研究提出一套结合编码器大语言模型与零样本立场检测的系统，追踪4000余家不同可信度英语网站的14.6万条新闻叙事传播，结合NETINF算法识别倾向性宣传网络与高影响力网站，助力虚假信息核查。
摘要：
理解误导性乃至完全虚假的信息如何进入新闻生态系统仍是一项艰巨挑战，这需要追踪叙事在数千家边缘及主流新闻网站间的传播方式。为此，我们提出了一套系统，该系统利用基于编码器的大语言模型和零样本立场检测技术，可规模化地识别并追踪新闻叙事及其态度，覆盖超过4000家事实不可靠、可信度混杂及事实可靠的英语新闻网站。我们在18个月的周期内运行该系统，追踪了14.6万条新闻报道的传播情况。通过借助NETINF算法的基于网络的推断方法，我们证明，新闻叙事的传播路径以及各网站对特定实体的立场，可用于揭示带有倾向性的宣传网络（例如反疫苗和反乌克兰宣传网络），并识别出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。我们希望提升分布式新闻生态的透明度，能够助力宣传和虚假信息的报道与事实核查工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
