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：批判性新闻阅读要求把握新闻的整体观点并引发批判性思考，这对通常从日常社交媒体获取信息的普通公众来说既有益又具挑战性。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批判性新闻阅读，但如何利用这些评论支持用户进行批判性新闻阅读的研究尚不充分。本文首先通过文献综述和预研究（N=12）得出用户对基于评论的批判性新闻阅读工具的需求，随后开发了CoNewsReader——一种基于大语言模型的交互式批判性新闻阅读工具。该工具通过评论中的补充信息帮助用户把握新闻观点，筛选对批判性阅读有用的评论，并基于评论生成问题引导用户进行批判性思考。我们开展的24名大学生被试内实验表明：与社交媒体中的基线新闻阅读界面相比，使用CoNewsReader的参与者获得了更具沉浸感的批判性阅读体验，在理解新闻和引发批判性思考方面表现更佳。最后，我们讨论了利用用户生成内容与机器生成内容辅助阅读任务的设计考量。