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REVIEW 2 major objections 4 minor 1 cited by

ComViewer: An Interactive Visual Tool to Help Viewers Seek Social Support in Online Mental Health Communities

T0 review · 2 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read ComViewer shows that an interactive, LLM-assisted interface for browsing mental-health forums helps viewers extract more informational support and feel more engaged than the standard list view.

desk verdict ComViewer is a well-built tool for a real gap—OMHC viewers who don't post—but the study's central quantitative outcome is probably inflated by the tool's own auto-generated summaries. read the letter →

arxiv 2411.19169 v1 pith:4LWWOUUR submitted 2024-11-28 cs.HC

Shiwei Wu , Mingxiang Wang , Chuhan Shi , Zhenhui Peng This is my paper · ORCID
classification cs.HC
keywords onlinementalhealthcommunitiessocialsupportinformationseekingsensemakingvisualizationlargelanguagemodelscirclepackingwithin-subjectsstudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

ComViewer aims to establish that passive viewers of online mental health communities—people who read rather than post—can be helped to seek social support by a purpose-built visual tool. From a ten-person formative study the authors distil five viewer challenges, from articulating needs in keywords to organizing fragmented advice and understanding unfamiliar terms, and they answer them with three linked panels: zoomable topic/post/comment circles with social-support filters, color-coded note-taking with LLM summaries, and an LLM-powered questioning panel. A within-subjects study with twenty participants found that with ComViewer viewers recorded significantly more meaningful informational-support points (5.25 vs 3.30, p<0.001), reported higher satisfaction and confidence, and rated the process more engaging and less cognitively demanding than with a baseline list-based interface. If the claim holds, the accumulated experience in mental-health forums becomes more usable for the large read-only population, and the design offers a template for turning noisy peer content into structured, personalized takeaways.

What carries the argument

The carrying mechanism is ComViewer's three-panel design mapped onto a four-stage information-seeking flow of search, read, organize, and digest. The Zoomable Posts panel implements the overview-first, zoom-and-filter, details-on-demand principle: an LDA topic model clusters search results into topic-level circles, each topic circle nests post circles sized by comment count, and each post circle nests comment circles; hovering reveals titles and similar posts, while bar charts filter posts and comments by predicted levels of seeking or providing emotional and informational support. The Note-taking panel lets viewers highlight text in colors, automatically collects highlights into matching folders, generates an editable LLM summary and a mind map, and links each note back to its source location. The Questioning panel generates recommended what/why/how questions for any selected content and provides editable LLM answers in a branching whiteboard layout. Together the panels turn an overwhelming linear feed into a structured, self-paced exploration with in-place comprehension support.

What would settle it

An ablation study that removes one of the three panels at a time—for example, replacing the zoomable circle packing with a plain list while keeping highlighting and questioning—would settle whether the full combination is responsible for the gains; if participants still record as many informational-support points and show the same engagement with the reduced interface, the three-panel design is not the cause. A longitudinal deployment that tracks returning users would also test whether the benefits persist after the novelty of the new tool wears off.

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Extended reading notes

Core claim

The paper's central claim, stated by the authors, is that ComViewer improves both the outcome and the experience of support seeking for OMHC viewers compared to a standard community interface. In a within-subjects study with 20 participants, ComViewer produced significantly more recorded meaningful informational-support points (mean 5.25 vs 3.30; p<0.001), significantly higher satisfaction and confidence in the received support, significantly higher engagement (concentration, clarity, doability, and a sense of discovery), and significantly lower perceived cognitive load for both searching and sensemaking. Participants also rated ComViewer more useful, easier to use, and more likely to be reused. The gain in recorded emotional-support points was not significant, and the authors attribute the improvements to the simplified visual search, the organization and summarization of highlighted content, and the ability to question unfamiliar content in place.

Load-bearing premise

The measured gains are credited to ComViewer's three panels on the assumption that the baseline interface differs from ComViewer only by those panels, so a whole-package difference or simple novelty could in principle explain the same results.

Editorial extensions

If this is right

  • OMHC interfaces should offer a hierarchical, filterable overview of posts and comments rather than only a linear list, since viewers' search and read steps benefit from the zoomable circle packing.
  • Interactive note-taking with color-coded organization and automatic summaries can lower the cognitive load of turning fragmented comments into a personal action plan.
  • LLM-generated recommended questions and answers can support sensemaking of user-generated community content, not just of LLM output, by resolving unfamiliar terms and prompting deeper reflection.
  • If ComViewer's effect transfers, viewers of online mental health communities would leave sessions with more concrete, actionable advice and more confidence in applying it to their own situations.
  • The three-panel design can be adapted to other online communities by swapping topic models, support classifiers, summary formats, and LLM prompts, per the paper's design considerations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural test of the paper's mechanism is an ablation that removes one panel at a time; participants' interview comments suggest the note-taking and questioning panels may contribute as much as the visual search to the recorded advice gain.
  • Because emotional-support points did not improve, a designer following this paper might add automatic highlighting of emotionally supportive sentences or an LLM that responds as an emotional supporter, to see whether both support types can be lifted.
  • The same zoom-organize-question loop should transfer to other large peer-advice corpora, such as design forums or technical Q&A sites, if the topic model and support classifiers are retrained, which would make the contribution a general information-seeking pattern rather than a mental-health-specific tool.
  • A longer deployment with returning users and behavioral logs, rather than a single-session questionnaire, would reveal whether the measured confidence and reduced mental load translate into sustained coping actions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. ComViewer is an interactive visual tool designed to help 'viewers' of online mental health communities (OMHCs) find and make sense of posts and comments that provide social support. The paper reports a formative study (N=10) that identifies five challenges in viewing OMHC content, derives four design requirements, and motivates three main panels: a Zoomable Posts panel for topic- and support-filtered exploration, a Note-taking panel with color-coded highlighting, automatic summarization, and mind-map generation, and a Questioning panel that uses an LLM to recommend questions and generate answers. The authors then describe a within-subjects user study (N=20) comparing ComViewer to a baseline OMHC-style interface without these panels. They report that ComViewer significantly increased the number of recorded meaningful informational-support points (5.25 vs. 3.30, p<0.001), improved satisfaction and confidence, raised engagement ratings, reduced perceived cognitive load, and led to more favorable usefulness and intention-to-use ratings. Qualitative interview data are used to explain these results and to derive three design considerations for future OMHC tools.

Significance. The paper addresses a real and underserved population—viewers who consume but do not post in OMHCs—and combines two currently relevant threads: information visualization for online communities and LLM-based sensemaking. The formative study is well conducted, the design requirements are concretely tied to the interface, the tool is substantial and the authors state the code will be open-sourced, and the statistical analyses are appropriate for the within-subjects design (paired t-test for the normally distributed informational-support count, Wilcoxon tests for ordinal ratings, and Latin-square counterbalancing). The authors are also transparent about limitations, including the novelty effect and the absence of an ablation study. However, as detailed in the major comments, the central quantitative claim—that ComViewer improves the outcome of support-seeking—is currently threatened by a measurement-validity problem: the outcome measure may count LLM-generated text rather than what participants actually gained from the community.

major comments (2)
  1. [§5.3, §5.4, §4.2.2, Appendix A.1] The RQ1 outcome measure is not shown to measure support-seeking rather than LLM output. In ComViewer, the Note-taking panel automatically generates a summary of all highlighted content (Section 4.2.2, Summary page), and the Appendix A.1 summarization prompt produces structured 'subtitle/content' units that map directly onto the coding unit of 'meaningful points' counted in Section 5.3. Participants were asked to write key points after each task (Section 5.4), and P5's quote in Section 6.1 ('I can highlight the content that may be helpful and get an automatically generated summary on all the highlighted content. This helped me easily summarize the take-away advice learned from the community.') shows that participants could produce many coded points by copying or lightly editing the generated summary. The baseline condition has no such generator. The paper does not report whether the key-point documents were compared with the persisted JSON summaries/answers stored per Section 4.3.3, nor whether the two coders were blind to condition. Therefore the significant increase in informational-support points (5.25 vs. 3.30, p<0.001) can be explained by the presence of an LLM text generator rather than by improved support-seeking. Please add a validation analysis (e.g., compute overlap between written points and stored JSON outputs; recode with condition-blinded coders; or run an ablation without automatic summaries) or explicitly soften the RQ1 claim to an analysis of what participants recorded, not what they learned.
  2. [§5.1, §7.3.2] The comparison is whole-package, not a test of the three panels. The baseline interface removes not only the three named panels but also highlighting, questioning, automatic summarization, support filtering, similar-post highlighting, and the mind-map view, and it also omits crowd-contributed tags from both arms (Section 5.1). The authors acknowledge in Section 7.3.2 that no ablation study was conducted. Consequently the significant differences in engagement, cognitive load, and perceived usefulness cannot be attributed to any specific panel, and the qualitative per-panel attributions in Section 6.3 are self-reports rather than controlled evidence. This is not a fatal flaw if the central claim is read as whole-tool effectiveness, but the contributions section and Sections 6.2 and 6.3 currently present stronger panel-level conclusions than the design supports. The manuscript should either add an ablation condition or clearly limit the claims to the combined system and describe the per-panel results as exploratory.
minor comments (4)
  1. [§6.1] In the RQ1 results text, the sentence 'Overall, participants with ComViewer (Mean = 5.25, SD = 2.25) are significantly more satisfied with the social support they have received' uses the informational-support point count rather than the satisfaction mean reported in Table 2 (ComViewer: 5.60, SD = 1.10; baseline: 4.40, SD = 1.54). Please correct this mismatch.
  2. [§5.3] The coding procedure states that two authors coded twenty documents, met to discuss disagreements, and then applied the scheme to the rest; it would be helpful to report an inter-coder reliability statistic (e.g., Cohen's kappa or Krippendorff's alpha) for the final coding of the meaningful points.
  3. [§4.3.1] The support classifiers were trained on r/depression data from [42,44] and applied to r/Anxiety without reporting any validation of that cross-community transfer; a brief justification or reference to prior cross-community evaluation would strengthen the trustworthiness of the support-filtering and support-distribution features.
  4. [§1] The phrase 'Our contributions to CSCW communities are three folds' should be 'threefold' (or 'three-fold'), and the three contributions are listed as full sentences in a way that would be clearer as parallel noun phrases.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the user-study outcome is independently coded from participant documents, and the self-cited classifiers are inputs to the tool, not load-bearing proof of effectiveness.

full rationale

The paper's central claim is an empirical within-subjects comparison, not a derived prediction. The RQ1 outcome (number of meaningful points about informational support) is coded from participants' written key-point documents, not read off ComViewer's fitted support labels; the ES/IS classifiers from [42,44] power the filter view but are not used to score the outcome. The baseline uses the same r/Anxiety corpus and the same Elasticsearch ranking, differing only in the three panels, so the comparison is a system-level test rather than a tautology. The self-cited classifiers are open-sourced models with reported accuracy trained on r/depression; they are inputs to the tool, not conclusions of this paper's evaluation, and they do not assume the target result. The acknowledged limitations in Section 7.3.2 (novelty effect, lack of an ablation study) are internal-validity and generalizability threats, not circularity. The auto-summary feature could in principle inflate the RQ1 count if participants copied generated summaries, but the paper does not define the outcome as equal to the generated summary, so this remains a measurement-validity caveat rather than a by-construction equivalence. No equation-level reduction or self-citation chain forces the reported results.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim relies on domain assumptions about support categories, classifier transfer, LLM reliability, and baseline validity, plus several hand-set parameters. These are not hidden, but they are load-bearing and not independently validated within the paper.

free parameters (4)
  • LDA topic count = 4
    Chosen by coherence analysis over search results; controls how posts are clustered in the Zoomable Posts panel.
  • Cosine similarity threshold for similar posts = 0.6
    Used to precompute similar post pairs in Elasticsearch; hand-set, affects which posts are highlighted as similar.
  • Number of top posts clustered = 150
    Only the 150 highest-scored posts per query are clustered and visualized; hand-set.
  • LLM model = gpt-4
    Chosen for note summarization and question answering; impacts quality of generated content but not the central quantitative comparison.
assumptions (4)
  • domain assumption Informational and emotional support are the two central categories of social support exchanged in OMHCs.
    The tool's filtering, outcome measures, and coding all rely on this binary taxonomy, cited to [54].
  • domain assumption Classifiers trained on r/depression by the authors' group transfer accurately to r/Anxiety for labeling sought and provided support.
    Section 4.3.1 applies models from [42,44] with reported accuracies 64-70% to a different subreddit without revalidation.
  • domain assumption The LLM-generated summaries, recommended questions, and answers are sufficiently accurate and safe for mental health support contexts.
    Used in Note-taking and Questioning panels; the paper itself notes hallucination risk in Section 7.3.3.
  • domain assumption The baseline interface (post list and post detail without panels and flairs) is an adequate control for isolating ComViewer's effects.
    The causal claim depends on this; Section 5.1 defines the baseline and Section 7.3.2 acknowledges the lack of ablation.

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Cite this review

Pith. "Pith review of ComViewer: An Interactive Visual Tool to Help Viewers Seek Social Support in Online Mental Health Communities." pith.science (2026). https://pith.science/paper/4LWWOUUR

@misc{pith2026241119169,
  author       = {Pith},
  title        = {Pith review of: ComViewer: An Interactive Visual Tool to Help Viewers Seek Social Support in Online Mental Health Communities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4LWWOUUR}},
  note         = {Machine review of arXiv:2411.19169}
}
read the original abstract

Online mental health communities (OMHCs) offer rich posts and comments for viewers, who do not directly participate in the communications, to seek social support from others' experience. However, viewers could face challenges in finding helpful posts and comments and digesting the content to get needed support, as revealed in our formative study (N=10). In this work, we present an interactive visual tool named ComViewer to help viewers seek social support in OMHCs. With ComViewer, viewers can filter posts of different topics and find supportive comments via a zoomable circle packing visual component that adapts to searched keywords. Powered by LLM, ComViewer supports an interactive sensemaking process by enabling viewers to interactively highlight, summarize, and question any community content. A within-subjects study (N=20) demonstrates ComViewer's strengths in providing viewers with a more simplified, more fruitful, and more engaging support-seeking experience compared to a baseline OMHC interface without ComViewer. We further discuss design implications for facilitating information-seeking and sense making in online mental health communities.

Figures

Figures reproduced from arXiv: 2411.19169 by the authors.

Figure 1
Figure 1. A common information-seeking flow in online mental health communities. ComViewer supports this flow with a Zoomable Posts Panel, a Note-taking Panel, and a Questioning Panel. We conducted a one-on-one formative study with each participant. The study started with querying participants’ mentally challenging issues in detail and introducing the task of browsing the posts and comments in r/Anxiety to seek social support… view at source ↗
Figure 2
Figure 2. ComViewer: (A) The Zoomable Posts panel consists of (A1) the Post-list View, which shows the [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. The Zoomable Posts Panel has three levels: (A) the topic level, (B) the post level, and (C) the comment [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Folder view: (A) Collection page (B) Summary page (C) Mind map page. (A1) Folder labels with the [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Questioning Panel: (A) Folder boards to show the list of questions. (B) Whiteboard with a question [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Procedure of the within-subjects user study. In each information-seeking task, participants explore [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]

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Cited by 1 Pith paper

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Reference graph

Works this paper leans on

73 extracted references · 50 canonical work pages · cited by 1 Pith paper

  1. [1]

    Eugene Agichtein, Carlos Castillo, Debora Donato, Aristides Gionis, and Gilad Mishne. 2008. Finding high-quality content in social media. In Proceedings of the 2008 international conference on web search and data mining . 183–194. , Vol. 1, No. 1, Article . Publication date: December 2025. ComViewer: An Interactive Visual Tool to Help Viewers Seek Social ...

    work page 2008

  2. [2]

    Shrey Bagroy, Ponnurangam Kumaraguru, and Munmun De Choudhury. 2017. A social media based index of mental well-being in college campuses. In Proceedings of the 2017 CHI Conference on Human factors in Computing Systems . 1634–1646

    work page 2017

  3. [3]

    David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993–1022

    2003

  4. [4]

    Virginia Braun and Victoria Clarke. 2012. Thematic analysis. American Psychological Association

    2012

  5. [5]

    Joseph Chee Chang, Nathan Hahn, and Aniket Kittur. 2016. Supporting mobile sensemaking through intentionally uncertain highlighting. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology . 61–68

    work page 2016

  6. [6]

    Chaomei Chen and Yue Yu. 2000. Empirical studies of information visualization: a meta-analysis. International Journal of Human-Computer Studies 53, 5 (2000), 851–866

    work page 2000

  7. [7]

    Munmun De Choudhury and Sushovan De. 2014. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. In Proceedings of the international AAAI conference on web and social media , Vol. 8. 71–80

    work page 2014

  8. [8]

    Munmun De Choudhury and Emre Kiciman. 2017. The language of social support in social media and its effect on suicidal ideation risk. In Proceedings of the international AAAI conference on web and social media , Vol. 11. 32–41

    work page 2017

Show all 73 references
  1. [9]

    Nicholas Diakopoulos, Amy Zhang, Dag Elgesem, and Andrew Salway. 2014. Identifying and analyzing moral evaluation frames in climate change blog discourse. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 8. 583–586

    2014

  2. [10]

    Elasticsearch. 2023. Elasticsearch. https://www.elastic.co/elasticsearch. 2023

    2023

  3. [11]

    Shogo Fujita, Tomohide Shibata, and Manabu Okumura. 2020. Diverse and non-redundant answer set extraction on community QA based on DPPs. arXiv preprint arXiv:2011.09140 (2020)

    2020 arXiv

  4. [13]

    Zelalem Gero, Chandan Singh, Yiqing Xie, Sheng Zhang, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2024. Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries. arXiv preprint arXiv:2403.01002 (2024)

    2024 arXiv

  5. [14]

    Saskia Gilmer, Avinash Bhat, Shuvam Shah, Kevin Cherry, Jinghui Cheng, and Jin L.C. Guo. 2023. SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization. Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 297 (oct 2023), 27 pages. https://doi.org/10.1145/3610088

    2023 doi

  6. [15]

    Ken Hinckley, Xiaojun Bi, Michel Pahud, and Bill Buxton. 2012. Informal information gathering techniques for active reading. In Proceedings of the SIGCHI conference on human factors in computing systems . 1893–1896

    2012

  7. [16]

    Enamul Hoque and Giuseppe Carenini. 2014. Convis: A visual text analytic system for exploring blog conversations. In Computer Graphics Forum, Vol. 33. Wiley Online Library, 221–230

    2014

  8. [17]

    Jina Huh, Bum Chul Kwon, Sung-Hee Kim, Sukwon Lee, Jaegul Choo, Jihoon Kim, Min-Je Choi, and Ji Soo Yi. 2016. Personas in online health communities. Journal of biomedical informatics 63 (2016), 212–225

    2016

  9. [18]

    Peiling Jiang, Jude Rayan, Steven P Dow, and Haijun Xia. 2023. Graphologue: Exploring large language model responses with interactive diagrams. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology . 1–20

    2023

  10. [19]

    Youwen Kang, Zhida Sun, Sitong Wang, Zeyu Huang, Ziming Wu, and Xiaojuan Ma. 2021. MetaMap: Supporting Visual Metaphor Ideation through Multi-dimensional Example-based Exploration. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (, Yokohama, Jap...

    2021

  11. [20]

    Meeyun Kim, Koustuv Saha, Munmun De Choudhury, and Daejin Choi. 2023. Supporters First: Understanding Online Social Support on Mental Health from a Supporter Perspective. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1–28

    2023

  12. [21]

    Johannes Knittel, Steffen Koch, Tan Tang, Wei Chen, Yingcai Wu, Shixia Liu, and Thomas Ertl. 2021. Real-time visual analysis of high-volume social media posts. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2021), 879–889

    2021

  13. [22]

    Bum Chul Kwon, Sung-Hee Kim, Sukwon Lee, Jaegul Choo, Jina Huh, and Ji Soo Yi. 2015. VisOHC: Designing visual analytics for online health communities. IEEE transactions on visualization and computer graphics 22, 1 (2015), 71–80

    2015

  14. [23]

    Bum Chul Kwon, Sung-Hee Kim, Sukwon Lee, Jaegul Choo, Jina Huh, and Ji Soo Yi. 2016. VisOHC: Designing Visual Analytics for Online Health Communities. IEEE Transactions on Visualization and Computer Graphics 22, 1 (2016), 71–80. https://doi.org/10.1109/TVCG.2015.2467555

    2016

  15. [24]

    Jill H Larkin and Herbert A Simon. 1987. Why a diagram is (sometimes) worth ten thousand words. Cognitive science 11, 1 (1987), 65–100. , Vol. 1, No. 1, Article . Publication date: December 2025. 28 Shiwei Wu, Mingxiang Wang, Chuhan Shi, and Zhenhui Peng

    1987

  16. [25]

    Guo Li, Xiaomu Zhou, Tun Lu, Jiang Yang, and Ning Gu. 2016. SunForum: Understanding Depression in a Chinese Online Community. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (San Francisco, California, USA) (CSCW ’16). Associ...

    2016

  17. [26]

    Yutong Li, Lu Chen, Aiwei Liu, Kai Yu, and Lijie Wen. 2024. ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary. arXiv preprint arXiv:2403.02574 (2024)

    2024 arXiv

  18. [27]

    Chengzhong Liu, Zeyu Huang, Dingdong Liu, Shixu Zhou, Zhenhui Peng, and Xiaojuan Ma. 2022. PlanHelper: Supporting Activity Plan Construction with Answer Posts in Community-based QA Platforms. Proc. ACM Hum.- Comput. Interact. 6, CSCW2, Article 454 (nov 2022), 26 pages. https:/...

    2022 doi

  19. [28]

    Chengzhong Liu, Shixu Zhou, Dingdong Liu, Junze Li, Zeyu Huang, and Xiaojuan Ma. 2023. CoArgue: Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA Platforms. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–17

    2023

  20. [29]

    Michael Xieyang Liu, Jane Hsieh, Nathan Hahn, Angelina Zhou, Emily Deng, Shaun Burley, Cynthia Taylor, Aniket Kittur, and Brad A Myers. 2019. Unakite: Scaffolding developers’ decision-making using the web. In Proceedings of the 32nd Annual ACM Symposium on User Interface Softw...

    2019

  21. [30]

    Michael Xieyang Liu, Aniket Kittur, and Brad A Myers. 2021. To reuse or not to reuse? A framework and system for evaluating summarized knowledge. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–35

    2021

  22. [31]

    Michael Xieyang Liu, Aniket Kittur, and Brad A Myers. 2022. Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–16

    2022

  23. [32]

    Michael Xieyang Liu, Andrew Kuznetsov, Yongsung Kim, Joseph Chee Chang, Aniket Kittur, and Brad A Myers. 2022. Wigglite: Low-cost information collection and triage. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 1–16

    2022

  24. [33]

    Michael Xieyang Liu, Tongshuang Wu, Tianying Chen, Franklin Mingzhe Li, Aniket Kittur, and Brad A Myers. 2024. Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models. In Proceedings of the CHI Conference on Human Factors in Co...

    2024

  25. [34]

    Ann Macaskill. 2013. The mental health of university students in the United Kingdom. British Journal of Guidance & Counselling 41, 4 (2013), 426–441

    2013

  26. [35]

    Jennifer Marlow and Laura Dabbish. 2014. From rookie to all-star: professional development in a graphic design social networking site. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing . 922–933

    2014

  27. [36]

    S Anne Moorhead, Diane E Hazlett, Laura Harrison, Jennifer K Carroll, Anthea Irwin, and Ciska Hoving. 2013. A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication. J Med Internet Res 15, 4 (23 Apr 2013)...

    2013 doi

  28. [37]

    Adi Omari, David Carmel, Oleg Rokhlenko, and Idan Szpektor. 2016. Novelty based ranking of human answers for community questions. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 215–224

    2016

  29. [38]

    Srishti Palani, Zijian Ding, Austin Nguyen, Andrew Chuang, Stephen MacNeil, and Steven P Dow. 2021. CoNotate: Suggesting queries based on notes promotes knowledge discovery. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–14

    2021

  30. [39]

    Sun Young Park. 2018. Social support mosaic: Understanding mental health management practice on college campus. In Proceedings of the 2018 Designing Interactive Systems Conference . 121–133

    2018

  31. [40]

    Jaakko Peltonen, Kseniia Belorustceva, and Tuukka Ruotsalo. 2017. Topic-relevance map: Visualization for improving search result comprehension. In Proceedings of the 22nd international conference on intelligent user interfaces . 611–622

    2017

  32. [41]

    Zhenhui Peng, Qiaoyi Chen, Zhiyu Shen, Xiaojuan Ma, and Antti Oulasvirta. 2024. DesignQuizzer: A Community- Powered Conversational Agent for Learning Visual Design. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 44 (apr 2024), 40 pages. https://doi.org/10.1145/3637321

    2024 doi

  33. [42]

    Zhenhui Peng, Qingyu Guo, Ka Wing Tsang, and Xiaojuan Ma. 2020. Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (, Honolulu, HI, U...

    2020

  34. [43]

    Zhenhui Peng, Yuzhi Liu, Hanqi Zhou, Zuyu Xu, and Xiaojuan Ma. 2022. CReBot: Exploring interactive question prompts for critical paper reading. International Journal of Human-Computer Studies 167 (2022), 102898

    2022

  35. [44]

    Zhenhui Peng, Xiaojuan Ma, Diyi Yang, Ka Wing Tsang, and Qingyu Guo. 2021. Effects of Support-Seekers’ Community Knowledge on Their Expressed Satisfaction with the Received Comments in Mental Health Communities. InProceedings of the 2021 CHI Conference on Human Factors in Comp...

    2021

  36. [45]

    Pendse, and Amit Sharma

    Yada Pruksachatkun, Sachin R. Pendse, and Amit Sharma. 2019. Moments of Change: Analyzing Peer-Based Cognitive Support in Online Mental Health Forums. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association ...

    2019

  37. [46]

    Pushshift. 2019. Reddit Statistics - pushshift.io. Retrieved June 1, 2019 from https://pushshift.io/

    2019

  38. [47]

    Reddit. 2023. Reddit. https://reddit.com. 2023

    2023

  39. [48]

    Liam Richards Maldonado, Azza Abouzied, and Nancy W. Gleason. 2023. ReaderQuizzer: Augmenting Research Papers with Just-In-Time Learning Questions to Facilitate Deeper Understanding. In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Soc...

    2023 doi

  40. [49]

    John Rooksby, Alistair Morrison, and Dave Murray-Rust. 2019. Student perspectives on digital phenotyping: The acceptability of using smartphone data to assess mental health. In Proceedings of the 2019 CHI conference on human factors in computing systems . 1–14

    2019

  41. [50]

    Amanda Ross, Victor L Willson, Amanda Ross, and Victor L Willson. 2017. Paired samples T-test. Basic and Advanced Statistical Tests: Writing Results Sections and Creating Tables and Figures (2017), 17–19

    2017

  42. [51]

    Koustuv Saha and Amit Sharma. 2020. Causal factors of effective psychosocial outcomes in online mental health communities. In Proceedings of the international AAAI conference on web and social media , Vol. 14. 590–601

    2020

  43. [52]

    Jaclyn Schildkraut, Evelyn S Sokolowski, and John Nicoletti. 2021. The survivor network: The role of shared experiences in mass shootings recovery. Victims & Offenders 16, 1 (2021), 20–49

    2021

  44. [53]

    Samuel Sanford Shapiro and Martin B Wilk. 1965. An analysis of variance test for normality (complete samples). Biometrika 52, 3-4 (1965), 591–611

    1965

  45. [54]

    Eva Sharma and Munmun De Choudhury. 2018. Mental health support and its relationship to linguistic accommodation in online communities. In Proceedings of the 2018 CHI conference on human factors in computing systems . 1–13

    2018

  46. [55]

    Rui Sheng, Leni Yang, Haotian Li, Yan Luo, Ziyang Xu, Zhilan Zhou, David Gotz, and Huamin Qu. 2023. Knowledge Compass: A Question Answering System Guiding Students with Follow-Up Question Recommendations. In Adjunct Proceedings of the 36th Annual ACM Symposium on User Interfac...

    2023

  47. [56]

    Chuhan Shi, Fei Nie, Yicheng Hu, Yige Xu, Lei Chen, Xiaojuan Ma, and Qiong Luo. 2022. MedChemLens: An Interactive Visual Tool to Support Direction Selection in Interdisciplinary Experimental Research of Medicinal Chemistry. IEEE Transactions on Visualization and Computer Graph...

    2022

  48. [57]

    Ben Shneiderman. 2003. The eyes have it: A task by data type taxonomy for information visualizations. In The craft of information visualization. Elsevier, 364–371

    2003

  49. [58]

    Sangho Suh, Bryan Min, Srishti Palani, and Haijun Xia. 2023. Sensecape: Enabling multilevel exploration and sensemaking with large language models. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 1–18

    2023

  50. [59]

    Thitaree Tanprasert, Sidney S Fels, Luanne Sinnamon, and Dongwook Yoon. 2024. Debate Chatbots to Facilitate Critical Thinking on YouTube: Social Identity and Conversational Style Make A Difference. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–24

    2024

  51. [60]

    Yamei Tu, Rui Qiu, Yu-Shuen Wang, Po-Yin Yen, and Han-Wei Shen. 2022. PhraseMap: Attention-based keyphrases recommendation for information seeking. IEEE Transactions on Visualization and Computer Graphics (2022)

    2022

  52. [61]

    Viswanath Venkatesh and Hillol Bala. 2008. Technology acceptance model 3 and a research agenda on interventions. Decision sciences 39, 2 (2008), 273–315

    2008

  53. [62]

    Tatiana A Vlahovic, Yi-Chia Wang, Robert E Kraut, and John M Levine. 2014. Support matching and satisfaction in an online breast cancer support community. In Proceedings of the sigchi conference on human factors in computing systems . 1625–1634

    2014

  54. [63]

    Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Mo Yu, Xiaojuan Ma, and Hongan Wang. 2021. Cass: Towards building a social-support chatbot for online health community. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–31

    2021

  55. [64]

    Johannes Wheeldon. 2011. Is a Picture Worth a Thousand Words? Using Mind Maps to Facilitate Participant Recall in Qualitative Research. Qualitative Report 16, 2 (2011), 509–522

    2011

  56. [65]

    Julia K Wolff, Florian Schmiedek, Annette Brose, and Ulman Lindenberger. 2013. Physical and emotional well-being and the balance of needed and received emotional support: Age differences in a daily diary study. Social Science & Medicine 91 (2013), 67–75

    2013

  57. [66]

    Robert F Woolson. 2007. Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials (2007), 1–3. , Vol. 1, No. 1, Article . Publication date: December 2025. 30 Shiwei Wu, Mingxiang Wang, Chuhan Shi, and Zhenhui Peng

    2007

  58. [67]

    Yingcai Wu, Nan Cao, David Gotz, Yap-Peng Tan, and Daniel A Keim. 2016. A survey on visual analytics of social media data. IEEE Transactions on Multimedia 18, 11 (2016), 2135–2148

    2016

  59. [68]

    Yingcai Wu, Shixia Liu, Kai Yan, Mengchen Liu, and Fangzhao Wu. 2014. Opinionflow: Visual analysis of opinion diffusion on social media. IEEE transactions on visualization and computer graphics 20, 12 (2014), 1763–1772

    2014

  60. [69]

    Anbang Xu and Brian Bailey. 2012. What do you think? A case study of benefit, expectation, and interaction in a large online critique community. In Proceedings of the acm 2012 conference on computer supported cooperative work . 295–304

    2012

  61. [70]

    Anbang Xu, Shih-Wen Huang, and Brian Bailey. 2014. Voyant: generating structured feedback on visual designs using a crowd of non-experts. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. 1433–1444

    2014

  62. [71]

    Ryan Yen, Nicole Sultanum, and Jian Zhao. 2024. To Search or To Gen? Exploring the Synergy between Generative AI and Web Search in Programming. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems . 1–8

    2024

  63. [72]

    Kangyu Yuan, Hehai Lin, Shilei Cao, Zhenhui Peng, Qingyu Guo, and Xiaojuan Ma. 2023. CriTrainer: An Adaptive Training Tool for Critical Paper Reading. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (San Francisco, CA, USA) (UIST ’23)....

    2023

  64. [73]

    Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023. Siren’s song in the AI ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219 (2023)

    2023 arXiv

  65. [74]

    subtitle

    Yu Zhang, Jingwei Sun, Li Feng, Cen Yao, Mingming Fan, Liuxin Zhang, Qianying Wang, Xin Geng, and Yong Rui. 2024. See Widely, Think Wisely: Toward Designing a Generative Multi-agent System to Burst Filter Bubbles. In Proceedings of the CHI Conference on Human Factors in Comput...

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.