CoNewsReader: Supporting Comprehensive Understanding and Raising Critical Thoughts on Social Media News Through Comments
Kangyu Yuan, Guanzheng Chen, Sizhe Liang, Hehai Lin, Qingyu Guo, Dingdong Liu, Xiaojuan Ma, Zhenhui Peng
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 2da7eb58-8c19-4fd8-9787-5cc78fb8df0eBuilds on22
- Grounding Multimodal Large Language Models to the WorldZhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao et al.ICLR 2024 · 1,170 citations
- Why Johnny Can't Prompt: How Non-AI Experts Try (and Fail) to Design LLM PromptsJ. D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, Qian YangCHI 2023 · 892 citations
- Human-AI Collaboration via Conditional Delegation: A Case Study of Content ModerationVivian Lai, Samuel Carton, Rajat Bhatnagar, Q. Vera Liao et al.CHI 2022 · 135 citations
- ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation SkillsThiemo Wambsganss, Tobias Kueng, Matthias Söllner, Jan Marco LeimeisterCHI 2021 · 126 citations
- AL: An Adaptive Learning Support System for Argumentation SkillsThiemo Wambsganss, Christina Niklaus, Matthias Cetto, Matthias Söllner et al.CHI 2020 · 98 citations
Related papers
- CriTrainer: An Adaptive Training Tool for Critical Paper ReadingKangyu Yuan, Hehai Lin, Shilei Cao, Zhenhui Peng et al.UIST 2023 · 21 citations
- SNS-Bench: Defining, Building, and Assessing Capabilities of Large Language Models in Social Networking ServicesHongcheng Guo, Yue Wang, Shaosheng Cao, Fei Zhao et al.ICML 2025
- Intelligent Support Engages Writers Through Relevant Cognitive ProcessesAndreas Göldi, Thiemo Wambsganss, Seyed Parsa Neshaei, Roman RietscheCHI 2024 · 20 citations
- ReadingQuizMaker: A Human-NLP Collaborative System that Supports Instructors to Design High-Quality Reading Quiz QuestionsXinyi Lu, Simin Fan, Jessica Houghton, Lu Wang et al.CHI 2023 · 43 citations
- Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection ToolLiudmila Zavolokina, Kilian Sprenkamp, Zoya Katashinskaya, Daniel Gordon Jones et al.CHI 2024 · 23 citations
