arxiv: 2604.27905 · v2 · submitted 2026-04-30 · 💻 cs.HC

Recognition: no theorem link

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

Authors on Pith no claims yet

Pith reviewed 2026-05-14 22:05 UTC · model grok-4.3

classification 💻 cs.HC

keywords critical news readingsocial media commentslarge language modelsuser interface designnews comprehensioncritical thinkinginteractive reading tools

0 comments

The pith

CoNewsReader uses LLM-processed comments to help users grasp news ideas and generate critical thoughts on social media.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CoNewsReader, an interactive tool that draws on comments beneath social media news posts to support critical news reading. It provides complementary details from comments to build a fuller picture of the story, filters out less useful comments, and automatically generates questions that prompt users to think more deeply and skeptically about the content. In a controlled within-subjects study with 24 university students, the tool produced higher engagement, better news comprehension, and more critical thoughts than a standard social-media reading interface. Requirements for the tool came from prior literature plus a formative study with twelve participants, after which the authors built the LLM-powered features and evaluated them directly.

Core claim

CoNewsReader is a comment-based interactive CNR tool powered by a large language model that supports 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, resulting in a more engaging CNR experience along with improved comprehension and critical-thought performance in a within-subjects study of 24 university students.

What carries the argument

LLM-driven comment filtering combined with automatic question generation that turns reader comments into structured support for holistic understanding and critical reflection.

If this is right

Complementary details pulled from comments help users build a more complete understanding of the reported event.
Automatically generated questions based on comments reliably prompt users to surface assumptions, biases, and alternative viewpoints.
Comment filtering reduces overload while preserving diverse reader perspectives that aid critical analysis.
The overall experience becomes measurably more engaging than scrolling a conventional social-media news feed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same comment-processing pipeline could be adapted to long-form articles or video transcripts where user comments also exist.
Repeated use might produce measurable gains in users' independent critical-thinking habits over weeks or months.
Designers could add user controls to tune the strictness of comment filtering or the depth of generated questions.
Real-time comment streams would require additional safeguards against rapidly changing or adversarial content.

Load-bearing premise

Benefits seen in a lab study with university students will appear for varied real-world users on live social media platforms while the underlying LLM filtering and question generation stay accurate and unbiased.

What would settle it

A field deployment on an actual social media platform with a broad user population that shows no measurable gain in comprehension or critical-thinking scores compared with the baseline interface.

Figures

Figures reproduced from arXiv: 2604.27905 by Dingdong Liu, Guanzheng Chen, Hehai Lin, Kangyu Yuan, Qingyu Guo, Sizhe Liang, Xiaojuan Ma, Zhenhui Peng.

**Figure 1.** Figure 1: General user scenario. A common social media news reading case reveals how readers use main points, critical-target comments view at source ↗

**Figure 2.** Figure 2: Design pipeline of CoNewsReader Manuscript submitted to ACM view at source ↗

**Figure 3.** Figure 3: illustrates the interface design of CoNewsReader. In addition to the content and comments of news that appears like those in other social media interfaces, CoNewsReader provides unique features tailored to the 3R reading process, which includes the key points of the news (Read content), comments filter (Read comment), and keywords with corresponding critical thinking questions (Reflect). We introduce the t… view at source ↗

**Figure 4.** Figure 4: NLP pipeline. News content and its first-level comments (A) are concatenated into a news-comment pair and input into the view at source ↗

**Figure 5.** Figure 5: Process of building up models. Two researchers first looked through 16 social media news and coded 240 comments. The view at source ↗

**Figure 6.** Figure 6: Experiment design and procedure. We conducted the experiment offline view at source ↗

**Figure 7.** Figure 7: (RQ1 results) The means, standard variation, and 95% confidence intervals of the rated scores about participants’ writing after view at source ↗

**Figure 8.** Figure 8: Usage frequency of main interface buttons in the between-subjects study with 24 participants. The button in Figure 3 b1 has view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CoNewsReader gives a clean lab demonstration of comment-plus-LLM support for critical news reading, but the benefits are shown only with 24 university students in a short session.

read the letter

The main takeaway is that the authors built a working system that pulls complementary details from comments, filters the useful ones, and generates questions to prompt critical thinking, then showed in a within-subjects study that it produced higher engagement, comprehension scores, and critical-thought measures than a plain social-media-style baseline. That combination of features is new enough to count as a contribution in the HCI news-reading space. The formative study with 12 people to derive the requirements is a sensible step, and the evaluation design is straightforward and reports clear differences on the measured outcomes. The paper stays grounded in the data it collected and does not over-reach in the abstract. The soft spots are exactly what the stress-test note flags. The sample is small and homogeneous, the setting is a lab rather than actual social media feeds, and there is no separate check on whether the LLM filtering and question generation stay accurate or unbiased when the topics and comment quality vary. Self-reported engagement and short-term tasks also leave open how much carries over to everyday use. Those limits are typical for this type of system paper, but they do cap how far the headline claim travels. The work is aimed at HCI researchers who build tools for information consumption and literacy. Anyone looking for a concrete example of comment-driven support plus LLM augmentation will find usable design details and comparison data. It is coherent on its own terms and shows honest engagement with the literature, so it deserves a serious referee even though it will likely need more diverse and longer-term validation before publication.

Referee Report

3 major / 2 minor

Summary. The paper presents CoNewsReader, an LLM-powered interactive tool that leverages social media comments to support critical news reading (CNR). Requirements are derived from prior literature and a separate formative study (N=12). The system provides complementary information from comments, filters useful comments, and generates questions to promote critical thinking. A within-subjects lab study with 24 university students reports that the tool yields higher engagement, better news comprehension, and more critical thoughts compared to a standard social-media-style baseline interface.

Significance. If the benefits hold beyond the tested setting, the work contributes concrete design patterns for integrating user-generated comments with LLM assistance to improve news literacy on social platforms. The separation of the formative study from the main evaluation avoids circularity and supplies direct comparative data via the within-subjects design.

major comments (3)

[§5] §5 (User Study): The evaluation uses a homogeneous sample of 24 university students in a controlled lab setting with no reported demographic diversity, prior knowledge variation, or long-term measures; this directly limits the load-bearing claim that benefits will generalize to diverse real-world social media users.
[§4] §4 (System Implementation): No separate validation or error analysis is reported for the LLM-based comment filtering and question-generation components across varied news topics or comment quality levels, leaving the reliability of the core technical features untested.
[§6] §6 (Discussion): The design considerations do not address potential demand characteristics of the lab study or how observed improvements might change in an uncontrolled field deployment, weakening the extrapolation from the central empirical result.

minor comments (2)

[Abstract] Abstract and §3: The baseline interface is referred to only generically as 'a baseline news reading interface in social media'; providing a brief description or screenshot reference would improve reproducibility.
[§5.2] §5.2: Task materials and news topics used in the study are not listed; including them would allow readers to assess topic-specific effects.

Simulated Author's Rebuttal

3 responses · 1 unresolved

Thank you for the constructive feedback. We address each major comment below, indicating where we will revise the manuscript to strengthen the presentation of limitations while preserving the integrity of our reported findings.

read point-by-point responses

Referee: [§5] §5 (User Study): The evaluation uses a homogeneous sample of 24 university students in a controlled lab setting with no reported demographic diversity, prior knowledge variation, or long-term measures; this directly limits the load-bearing claim that benefits will generalize to diverse real-world social media users.

Authors: We agree the sample is limited to university students in a lab setting, which constrains generalizability. This is typical for initial controlled HCI evaluations. We will revise the limitations and discussion sections to explicitly state this constraint, avoid overgeneralizing claims, and outline future work with diverse populations and longitudinal designs. The within-subjects comparative results remain valid evidence for the tool's effects under the tested conditions. revision: partial
Referee: [§4] §4 (System Implementation): No separate validation or error analysis is reported for the LLM-based comment filtering and question-generation components across varied news topics or comment quality levels, leaving the reliability of the core technical features untested.

Authors: We acknowledge the lack of dedicated validation for the LLM modules. The components were derived from the formative study and the main evaluation provides indirect support via user outcomes. We will add an appendix with post-hoc error analysis of LLM outputs from the study sessions (e.g., filtering accuracy and question relevance) and note any topic-specific limitations observed. revision: partial
Referee: [§6] §6 (Discussion): The design considerations do not address potential demand characteristics of the lab study or how observed improvements might change in an uncontrolled field deployment, weakening the extrapolation from the central empirical result.

Authors: We will expand the discussion to cover demand characteristics of the lab setting and contrast potential outcomes in field deployments, citing relevant HCI literature on ecological validity. This will include more cautious framing of the results without altering the reported data. revision: yes

standing simulated objections not resolved

The homogeneous student sample and absence of long-term measures cannot be retroactively corrected without new empirical studies.

Circularity Check

0 steps flagged

No circularity in empirical design and evaluation chain

full rationale

The paper derives user requirements from a separate formative study (N=12) and literature review, builds CoNewsReader accordingly, and evaluates it through an independent within-subjects lab study (N=24) whose measured outcomes (engagement, comprehension, critical thoughts) constitute the central claims. No equations, fitted parameters, self-definitional loops, or load-bearing self-citations reduce any result to its inputs by construction; the evaluation data are collected externally to the design inputs, rendering the chain self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on domain assumptions about the value of comments for CNR and the reliability of LLM outputs for filtering and question generation; these are tested via user studies but not independently validated outside the reported experiments.

axioms (2)

domain assumption Comments under news posts supply complementary information and diverse critical thoughts that aid comprehensive understanding
Stated in abstract as motivation drawn from literature and formative study (N=12)
domain assumption LLM can accurately filter useful comments and generate relevant critical-thinking questions from comment content
Core to CoNewsReader design; assumed to work sufficiently for the reported benefits

pith-pipeline@v0.9.0 · 5521 in / 1393 out tokens · 38446 ms · 2026-05-14T22:05:06.730626+00:00 · methodology

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Reference graph

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