Critical news reading (CNR), which requires grasping the holistic ideas of and raising critical thoughts on the news, is beneficial yet challenging for general people who usually get information on daily social media. Comments under the news can aid CNR by providing complementary information and other readers' diverse and critical thoughts. However, it is under-investigated how to leverage these comments to support users in CNR. In this paper, we first derive user requirements for a comment-based CNR tool from literature and a formative study (N=12). Then, we develop CoNewsReader, a comment-based interactive CNR tool powered by a large language model. CoNewsReader supports users in grasping the news idea with complementary information from comments, filtering useful comments for CNR, and getting questions generated based on the comments to conduct critical thinking. Our within-subjects study with 24 university students indicates that compared to a baseline news reading interface in social media, participants with CoNewsReader have a more engaging CNR experience and perform better on comprehending the news and raising critical thoughts. We discuss design considerations for supporting reading tasks with user- and machine-generated content.

翻译：批判性新闻阅读（CNR）要求把握新闻的整体观点并提出批判性思考，这对于通常通过日常社交媒体获取信息的普通公众而言既有益处又具挑战性。新闻下方的评论可通过提供补充信息及其他读者的多元批判性观点来辅助CNR。然而，如何利用这些评论支持用户开展CNR的研究尚不充分。本文首先通过文献分析和形成性研究（N=12）提炼出基于评论的CNR工具的用户需求。随后，我们开发了CoNewsReader——一种基于大语言模型的交互式评论CNR工具。该工具通过评论中的补充信息帮助用户把握新闻要点，筛选对CNR有用的评论，并基于评论生成引导批判性思考的问题。我们对24名大学生开展的受试者内实验表明，与社交媒体中基准新闻阅读界面相比，使用CoNewsReader的参与者获得了更沉浸的CNR体验，并在新闻理解与批判性思考激发方面表现更优。最后，我们探讨了利用用户生成内容与机器生成内容支持阅读任务的设计考量。