CoNewsReader: Supporting Comprehensive Understanding and Raising Critical Thoughts on Social Media News Through Comments

Fuente: arXiv

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Bibliographic Details
Main Authors:	Yuan, Kangyu, Chen, Guanzheng, Liang, Sizhe, Lin, Hehai, Guo, Qingyu, Liu, Dingdong, Ma, Xiaojuan, Peng, Zhenhui
Format:	Preprint
Published:	2026
Subjects:	Human-Computer Interaction
Online Access:	Acceder al recurso
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_version_	1866914562373058560
author	Yuan, Kangyu Chen, Guanzheng Liang, Sizhe Lin, Hehai Guo, Qingyu Liu, Dingdong Ma, Xiaojuan Peng, Zhenhui
author_facet	Yuan, Kangyu Chen, Guanzheng Liang, Sizhe Lin, Hehai Guo, Qingyu Liu, Dingdong Ma, Xiaojuan Peng, Zhenhui
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2604_27905
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	CoNewsReader: Supporting Comprehensive Understanding and Raising Critical Thoughts on Social Media News Through Comments Yuan, Kangyu Chen, Guanzheng Liang, Sizhe Lin, Hehai Guo, Qingyu Liu, Dingdong Ma, Xiaojuan Peng, Zhenhui Human-Computer Interaction 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.
title	CoNewsReader: Supporting Comprehensive Understanding and Raising Critical Thoughts on Social Media News Through Comments
topic	Human-Computer Interaction
url	https://arxiv.org/abs/2604.27905