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arxiv: 2507.08110 · v4 · submitted 2025-07-10 · 💻 cs.CY · cs.SI

AI Feedback Enhances Community-Based Content Moderation through Engagement with Counterarguments

Saeedeh Mohammadi , Taha Yasseri This is my paper

Pith reviewed 2026-05-19 05:18 UTC · model grok-4.3

classification 💻 cs.CY cs.SI
keywords AI feedbackcommunity moderationCommunity Notesargumentative feedbackcontent moderationhuman-AI collaborationcrowdsourced fact-checkingnote quality
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0 comments X

The pith

AI-generated argumentative feedback produces the largest gains in quality for community-sourced notes.

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

The paper tests an AI-assisted system in which people writing Community Notes receive one of three kinds of AI feedback—supportive, neutral, or argumentative—and then revise their notes. Quality ratings rise in all cases, yet the biggest lift occurs when the feedback presents counterarguments that must be addressed. This result points to a practical way for hybrid human-AI moderation to reduce partisan bias and speed verification on platforms that rely on crowdsourced fact-checking. The work shows that direct engagement with opposing views, rather than simple endorsement or neutrality, drives measurable improvement in the final notes.

Core claim

Participants who received AI-generated argumentative feedback on their Community Notes and revised accordingly produced notes of measurably higher quality than those who received supportive or neutral feedback. The improvement is attributed to the requirement that writers directly engage with counterarguments, which leads to more balanced and better-supported content. The study therefore concludes that an AI-assisted hybrid framework can enhance the effectiveness of community-based moderation by incorporating diverse perspectives through targeted feedback.

What carries the argument

The AI-generated argumentative feedback loop that presents counterarguments to a draft note and prompts the writer to revise in response.

If this is right

  • Note quality increases after any AI feedback but rises most after argumentative feedback.
  • Direct engagement with opposing views improves the balance and support of crowdsourced fact-checks.
  • Hybrid human-AI systems can address delays and partisan bias in existing community moderation.
  • Design choices that prioritize counterargument feedback yield stronger collective intelligence outcomes.

Where Pith is reading between the lines

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

  • Platforms could insert short AI feedback steps into existing note-writing interfaces without changing user incentives.
  • The same feedback mechanism might transfer to other crowdsourced verification tasks such as Wikipedia edit reviews or citizen-science data labeling.
  • Long-term deployment would require monitoring whether repeated exposure to argumentative feedback changes writers' willingness to contribute at all.

Load-bearing premise

The observed quality gains come from genuine engagement with the counterarguments rather than from the simple act of revising or from unmeasured differences in how the feedback types were generated.

What would settle it

A follow-up trial in which participants revise notes after receiving only generic revision prompts (no content-specific feedback) and still show quality gains equal to or larger than those produced by argumentative feedback.

Figures

Figures reproduced from arXiv: 2507.08110 by Saeedeh Mohammadi, Taha Yasseri.

Figure 1
Figure 1. Figure 1: The experimental workflow illustrates the process of note creation, feedback assignment, and evaluation. Participants (self-identified as Democrats or Republicans) wrote initial notes to provide context on posts authored by either Democrats or Republicans. They then received randomly assigned feedback varying in type (supportive, neutral, or argumentative) and source label (AI agent or human expert). After… view at source ↗
Figure 2
Figure 2. Figure 2: Examples of notes with high and low engagement in response to argumentative feedback. (a) A note that improved following argumentative feedback. (b) A note that declined following argumentative feedback To measure engagement, we introduce the Feedback Acceptance rate (F A), a metric that quantifies the 7 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Logistic regression for the note rating improvement. Coefficient estimates from the logistic regression model for note rating improvement as a function of F A. Points represent estimated log-odds, with horizontal bars indicating 95% confidence intervals. A vertical dashed line at 0 denotes the null effect. Estimates are grouped by rater affiliation, with blue indicating Democrats and red indicating Republi… view at source ↗
Figure 4
Figure 4. Figure 4: Bar plot of OLS model for F A. (a) Model 1 includes feedback type (Neutral as the reference), post type (Republican as the reference), and participant partisanship (Republican as the reference). (b) Model 2 includes feedback type, the alignment between participant and post￾partisanship (Co- vs. Cross-partisan), and their interaction, with Cross-partisan × Neutral as the reference category. The plots show e… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of F A values by treatment condition and feedback source for different source labels. Mean F A values are shown for each feedback type and the alignment between the participant and the post partisanship (Co- vs. Cross-partisan), with bars coloured by the feedback source (AI vs. Human). Error bars represent the standard error of the mean. To construct the complete model and evaluate the effect of… view at source ↗
Figure 6
Figure 6. Figure 6: Logistic regression for the note rating improvements based on feedback type and F A. Coefficient estimates from a logistic regression model predicting ID and IR using Feedback Acceptance rate (F A), feedback type (with Neutral as the reference), and their interaction. Points represent estimated coefficients (log-odds), with horizontal bars indicating 95% confidence intervals. A vertical dashed line at 0 de… view at source ↗
Figure 7
Figure 7. Figure 7: AI-generated feedback across treatment conditions. An example of a note created in the experiment and the responses the AI provides in each treatment. stance. In the Neutral group, the AI rephrased the note without introducing contradictions or additional evidence. In the Support group, the AI reinforced and expanded on the participants’ original arguments. Examples of AI-generated feedback for each condit… view at source ↗
read the original abstract

Today, social media platforms are significant sources of news and political communication, but their role in spreading misinformation has raised significant concerns. In response, these platforms have implemented various content moderation strategies. One such method, Community Notes (formerly Birdwatch) on X (formerly Twitter), relies on crowdsourced fact-checking and has gained traction. However, it faces challenges such as partisan bias and delays in verification. This study explores an AI-assisted hybrid moderation framework in which participants receive AI-generated feedback, supportive, neutral, or argumentative, on their notes and are asked to revise them accordingly. The results show that incorporating feedback improves note quality, with the most substantial gains coming from argumentative feedback. This underscores the value of diverse perspectives and direct engagement in human-AI collective intelligence. The research contributes to ongoing discussions about AI's role in political content moderation, highlighting the potential of generative AI and the importance of informed design.

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.

Referee Report

2 major / 1 minor

Summary. The paper proposes an AI-assisted hybrid moderation framework for Community Notes on X, in which participants receive one of three types of AI-generated feedback (supportive, neutral, or argumentative) on their notes and are asked to revise them. It reports that feedback incorporation improves note quality, with the largest gains under argumentative feedback, and argues this demonstrates the value of engaging with counterarguments in human-AI collective intelligence for content moderation.

Significance. If the central empirical result holds after addressing design issues, the work would be moderately significant for platform moderation research. It offers a concrete test of how generative AI can supply diverse perspectives to crowdsourced fact-checking, potentially addressing delays and partisan bias in systems like Community Notes, and contributes to broader discussions of AI-augmented collective intelligence in political communication.

major comments (2)
  1. [Methods] The experimental design (likely described in the Methods section) compares three feedback conditions but omits a no-feedback revision control arm and does not report measures or statistical controls for revision effort, time-on-task, or number of edits. This leaves open the possibility that quality gains attributed to argumentative feedback are artifacts of generic revision incentives rather than engagement with counterarguments, directly undermining the causal claim in the abstract and results.
  2. [Results] The Results section (and abstract) reports directional improvements in note quality without providing sample size, statistical tests, effect sizes, controls for confounds, or the operational definition and measurement of 'note quality.' These omissions make it impossible to assess whether the data support the claim that argumentative feedback produces the most substantial gains.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by briefly stating the sample size, how note quality was scored, and the key statistical result supporting the 'most substantial gains' claim.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their detailed and constructive comments. We address each major concern point by point below and have revised the manuscript to improve reporting and acknowledge design limitations where possible.

read point-by-point responses

  1. Referee: [Methods] The experimental design (likely described in the Methods section) compares three feedback conditions but omits a no-feedback revision control arm and does not report measures or statistical controls for revision effort, time-on-task, or number of edits. This leaves open the possibility that quality gains attributed to argumentative feedback are artifacts of generic revision incentives rather than engagement with counterarguments, directly undermining the causal claim in the abstract and results.

    Authors: We agree that a no-feedback control arm would strengthen causal inference by separating the effects of receiving feedback from the effects of revision alone. Our design prioritized comparisons across feedback types (supportive, neutral, argumentative), all of which prompted revision, allowing relative differences in quality gains to be attributed to feedback content. We have added an explicit discussion of this design decision and its implications to the Limitations section. However, we did not collect time-on-task or edit-count data during the original experiment and therefore cannot add statistical controls for revision effort. We have updated the Limitations section to note this gap and recommend that future studies include such measures. revision: partial

  2. Referee: [Results] The Results section (and abstract) reports directional improvements in note quality without providing sample size, statistical tests, effect sizes, controls for confounds, or the operational definition and measurement of 'note quality.' These omissions make it impossible to assess whether the data support the claim that argumentative feedback produces the most substantial gains.

    Authors: We have revised the Results section to include the missing details: the total sample size, the operational definition of note quality (a composite of accuracy, clarity, and sourcing rated by blinded coders), the statistical tests performed (including pre-post comparisons and between-condition ANOVA), effect sizes, and basic demographic controls. These elements were summarized in supplementary materials but have now been integrated into the main text and abstract for transparency. revision: yes

standing simulated objections not resolved
  • The original experiment did not collect data on revision effort, time-on-task, or number of edits, so these specific controls cannot be added retrospectively.

Circularity Check

0 steps flagged

No circularity: empirical experiment with independent outcome measures

full rationale

The paper describes a controlled experiment in which participants revise Community Notes after receiving one of three AI feedback conditions (supportive, neutral, argumentative) and quality is then scored by independent raters. No equations, fitted parameters, or first-principles derivations are present; results are obtained from new data collection rather than by re-expressing inputs. No self-citation is invoked to justify uniqueness or to close a logical loop. The design is therefore self-contained against external benchmarks and does not reduce any claimed result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based solely on the abstract; no explicit free parameters, axioms, or invented entities are described in the available text.

pith-pipeline@v0.9.0 · 5686 in / 1100 out tokens · 39722 ms · 2026-05-19T05:18:27.702156+00:00 · methodology

discussion (0)

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Characterizing AI Fact-Checkers and Their Contributions on Community Notes

    cs.CY 2026-05 unverdicted novelty 7.0

    AI writers account for 14.2% of Community Notes submissions with high responsiveness and coverage but lower helpfulness classification rates than human experts.

  2. Beyond Community Notes: A Framework for Understanding and Building Crowdsourced Context Systems for Social Media

    cs.HC 2025-09 conditional novelty 6.0

    The authors conduct a systematic literature review and real-world analysis to define Crowdsourced Context Systems and map a six-aspect design space with normative implications.

Reference graph

Works this paper leans on

67 extracted references · 67 canonical work pages · cited by 2 Pith papers · 4 internal anchors

  1. [1]

    Exposure to ideologically diverse news and opinion on facebook.Science, 348(6239):1130–1132, 2015

    Eytan Bakshy, Solomon Messing, and Lada A Adamic. Exposure to ideologically diverse news and opinion on facebook.Science, 348(6239):1130–1132, 2015

    work page 2015

  2. [2]

    Gore, impeachment, and beyond

    Cass R Sunstein.Echo chambers: Bush v. Gore, impeachment, and beyond. Princeton University Press Princeton, NJ, 2001

    work page 2001

  3. [3]

    How the news media activate public expression and influence national agendas.Science, 358(6364):776–780, 2017

    Gary King, Benjamin Schneer, and Ariel White. How the news media activate public expression and influence national agendas.Science, 358(6364):776–780, 2017

    work page 2017

  4. [4]

    Social media and fake news in the 2016 election.Journal of economic perspectives, 31(2):211–236, 2017

    Hunt Allcott and Matthew Gentzkow. Social media and fake news in the 2016 election.Journal of economic perspectives, 31(2):211–236, 2017. 19

    work page 2016

  5. [5]

    Misremembering brexit: Partisan bias and individual predictors of false memories for fake news stories among brexit voters.Memory, 29(5):587– 604, 2021

    Ciara M Greene, Robert A Nash, and Gillian Murphy. Misremembering brexit: Partisan bias and individual predictors of false memories for fake news stories among brexit voters.Memory, 29(5):587– 604, 2021

    work page 2021

  6. [6]

    Director-general speeches: Munich security conference

    World Health Organization. Director-general speeches: Munich security conference. Accessed: April 3, 2024

    work page 2024

  7. [7]

    Mobilising the mob: The multifaceted role of social media in the january 6th us capitol attack.Javnost-The Public, 32(1):33–50, 2025

    Ofra Klein. Mobilising the mob: The multifaceted role of social media in the january 6th us capitol attack.Javnost-The Public, 32(1):33–50, 2025

    work page 2025

  8. [8]

    More speech and fewer mistakes.https://about.fb.com/news/2025/01/ meta-more-speech-fewer-mistakes/, 2025

    Joel Kaplan. More speech and fewer mistakes.https://about.fb.com/news/2025/01/ meta-more-speech-fewer-mistakes/, 2025. Accessed: (February 25, 2025)

    work page 2025

  9. [9]

    Accessed: April 3, 2024

    X.com community notes guide.https://communitynotes.x.com/guide/en. Accessed: April 3, 2024

    work page 2024

  10. [10]

    The quest to automate fact-checking

    Naeemul Hassan, Bill Adair, James T Hamilton, Chengkai Li, Mark Tremayne, Jun Yang, and Cong Yu. The quest to automate fact-checking. InProceedings of the 2015 computation+ journalism symposium. Citeseer, 2015

    work page 2015

  11. [11]

    Emily Vogels, Andrew Perrin, and Monica Anderson. Most americans think social me- dia sites censor political viewpoints.https://www.pewresearch.org/internet/2020/08/19/ most-americans-think-social-media-sites-censor-political-viewpoints/, 2020. Accessed: February 26, 2025

    work page 2020

  12. [12]

    Can crowdsourcing rescue the social marketplace of ideas?Com- munications of the ACM, 66(9):42–45, 2023

    Taha Yasseri and Filippo Menczer. Can crowdsourcing rescue the social marketplace of ideas?Com- munications of the ACM, 66(9):42–45, 2023

    work page 2023

  13. [13]

    Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in twitter’s birdwatch crowdsourced fact-checking program

    Jennifer Allen, Cameron Martel, and David G Rand. Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in twitter’s birdwatch crowdsourced fact-checking program. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–19, 2022

    work page 2022

  14. [14]

    Rated not helpful; how x’s community notes system falls short on misleading election claims, 2024

    Center for Countering Digital Hate. Rated not helpful; how x’s community notes system falls short on misleading election claims, 2024. New research by CCDH shows that X’s Community Notes are failing to counter false and misleading claims about the US election

    work page 2024

  15. [15]

    arXiv:2502.08841 , year=

    Bao Tran Truong, Sangyeon Kim, Gianluca Nogara, Enrico Verdolotti, Erfan Samieyan Sahneh, Florian Saurwein, Natascha Just, Luca Luceri, Silvia Giordano, and Filippo Menczer. Delayed takedown of illegal content on social media makes moderation ineffective.arXiv preprint arXiv:2502.08841, 2025

    work page arXiv 2025

  16. [16]

    Human-in-the-loop artificial intelligence for fighting online misinformation: Challenges and opportunities.IEEE Data Eng

    Gianluca Demartini, Stefano Mizzaro, and Damiano Spina. Human-in-the-loop artificial intelligence for fighting online misinformation: Challenges and opportunities.IEEE Data Eng. Bull., 43(3):65–74, 2020. 20

    work page 2020

  17. [17]

    "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection

    William Yang Wang. ”liar, liar pants on fire”: A new benchmark dataset for fake news detection.arXiv preprint arXiv:1705.00648, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  18. [18]

    FEVER : a Large-scale Dataset for Fact Extraction and VER ification

    James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. Fever: a large-scale dataset for fact extraction and verification.arXiv preprint arXiv:1803.05355, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  19. [19]

    Directions in abusive language training data, a systematic review: Garbage in, garbage out.Plos one, 15(12):e0243300, 2020

    Bertie Vidgen and Leon Derczynski. Directions in abusive language training data, a systematic review: Garbage in, garbage out.Plos one, 15(12):e0243300, 2020

    work page 2020

  20. [20]

    Like trainer, like bot? inheritance of bias in algorithmic content moderation

    Reuben Binns, Michael Veale, Max Van Kleek, and Nigel Shadbolt. Like trainer, like bot? inheritance of bias in algorithmic content moderation. InSocial Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part II 9, pages 405–415. Springer, 2017

    work page 2017

  21. [21]

    How large language models can reshape collective intelligence.Nature human behaviour, 8(9):1643–1655, 2024

    Jason W Burton, Ezequiel Lopez-Lopez, Shahar Hechtlinger, Zoe Rahwan, Samuel Aeschbach, Michiel A Bakker, Joshua A Becker, Aleks Berditchevskaia, Julian Berger, Levin Brinkmann, et al. How large language models can reshape collective intelligence.Nature human behaviour, 8(9):1643–1655, 2024

    work page 2024

  22. [22]

    Ai-enhanced collective intelligence.Patterns, 5(11), 2024

    Hao Cui and Taha Yasseri. Ai-enhanced collective intelligence.Patterns, 5(11), 2024

    work page 2024

  23. [23]

    Llm-generated messages can persuade humans on policy issues.Nature Communications, 16(1):6037, 2025

    Hui Bai, Jan G Voelkel, Shane Muldowney, Johannes C Eichstaedt, and Robb Willer. Llm-generated messages can persuade humans on policy issues.Nature Communications, 16(1):6037, 2025

    work page 2025

  24. [24]

    Evaluating the persuasive influence of political microtargeting with large language models.Proceedings of the National Academy of Sciences, 121(24):e2403116121, 2024

    Kobi Hackenburg and Helen Margetts. Evaluating the persuasive influence of political microtargeting with large language models.Proceedings of the National Academy of Sciences, 121(24):e2403116121, 2024

    work page 2024

  25. [25]

    Large language models can rate news outlet credibility.arXiv e-prints, pages arXiv–2304, 2023

    Kai-Cheng Yang and Filippo Menczer. Large language models can rate news outlet credibility.arXiv e-prints, pages arXiv–2304, 2023

    work page 2023

  26. [26]

    The perils and promises of fact-checking with large language models.Frontiers in Artificial Intelligence, 7:1341697, 2024

    Dorian Quelle and Alexandre Bovet. The perils and promises of fact-checking with large language models.Frontiers in Artificial Intelligence, 7:1341697, 2024

    work page 2024

  27. [27]

    Leveraging chatgpt for efficient fact-checking.PsyArXiv

    Emma Hoes, Sacha Altay, and Juan Bermeo. Leveraging chatgpt for efficient fact-checking.PsyArXiv. April, 3, 2023

    work page 2023

  28. [28]

    In generative ai we trust: can chatbots effectively verify political information? Journal of Computational Social Science, 8(1):15, 2025

    Elizaveta Kuznetsova, Mykola Makhortykh, Victoria Vziatysheva, Martha Stolze, Ani Baghumyan, and Aleksandra Urman. In generative ai we trust: can chatbots effectively verify political information? Journal of Computational Social Science, 8(1):15, 2025

    work page 2025

  29. [29]

    arXiv preprint arXiv:2403.11169 , year=

    Xinyi Zhou, Ashish Sharma, Amy X Zhang, and Tim Althoff. Correcting misinformation on social media with a large language model.arXiv preprint arXiv:2403.11169, 2024. 21

    work page arXiv 2024

  30. [30]

    Supernotes: Driving consensus in crowd-sourced fact-checking

    Soham De, Michiel A Bakker, Jay Baxter, and Martin Saveski. Supernotes: Driving consensus in crowd-sourced fact-checking. InProceedings of the ACM on Web Conference 2025, pages 3751–3761, 2025

    work page 2025

  31. [31]

    arXiv preprint arXiv:2509.11052 , year=

    Shuning Zhang, Linzhi Wang, Dai Shi, Yuwei Chuai, Jingruo Chen, Yunyi Chen, Yifan Wang, Yating Wang, Xin Yi, and Hewu Li. Commenotes: Synthesizing organic comments to support community-based fact-checking.arXiv preprint arXiv:2509.11052, 2025

    work page arXiv 2025

  32. [32]

    Zhuoran Lu, Patrick Li, Weilong Wang, and Ming Yin. The effects of ai-based credibility indicators on the detection and spread of misinformation under social influence.Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2):1–27, 2022

    work page 2022

  33. [33]

    Believe it or not: designing a human-ai partnership for mixed-initiative fact-checking

    An T Nguyen, Aditya Kharosekar, Saumyaa Krishnan, Siddhesh Krishnan, Elizabeth Tate, Byron C Wallace, and Matthew Lease. Believe it or not: designing a human-ai partnership for mixed-initiative fact-checking. InProceedings of the 31st annual ACM symposium on user interface software and tech- nology, pages 189–199, 2018

    work page 2018

  34. [34]

    Content moderation, ai, and the question of scale.Big Data & Society, 7(2):5, 2020

    Tarleton Gillespie. Content moderation, ai, and the question of scale.Big Data & Society, 7(2):5, 2020

    work page 2020

  35. [35]

    A new sociology of humans and machines.Nature Human Behaviour, 8(10):1864–1876, 2024

    Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli, and Tobias Werner. A new sociology of humans and machines.Nature Human Behaviour, 8(10):1864–1876, 2024

    work page 2024

  36. [36]

    The role of explainability in collaborative human-ai disinformation detection

    Vera Schmitt, Luis-Felipe Villa-Arenas, NIls Feldhus, Joachim Meyer, Robert P Spang, and Sebastian M¨ oller. The role of explainability in collaborative human-ai disinformation detection. InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pages 2157–2174, 2024

    work page 2024

  37. [37]

    Automation bias in intelligent time critical decision support systems

    Mary L Cummings. Automation bias in intelligent time critical decision support systems. InDecision making in aviation, pages 289–294. Routledge, 2017

    work page 2017

  38. [38]

    Fact-checking in- formation from large language models can decrease headline discernment.Proceedings of the National Academy of Sciences, 121(50):e2322823121, 2024

    Matthew R DeVerna, Harry Yaojun Yan, Kai-Cheng Yang, and Filippo Menczer. Fact-checking in- formation from large language models can decrease headline discernment.Proceedings of the National Academy of Sciences, 121(50):e2322823121, 2024

    work page 2024

  39. [39]

    Exploring the use of personalized ai for identifying misinformation on social media

    Farnaz Jahanbakhsh, Yannis Katsis, Dakuo Wang, Lucian Popa, and Michael Muller. Exploring the use of personalized ai for identifying misinformation on social media. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–27, 2023

    work page 2023

  40. [40]

    Representation Engineering: A Top-Down Approach to AI Transparency

    Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top- down approach to ai transparency.arXiv preprint arXiv:2310.01405, 2023. 22

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  41. [41]

    Yizhou Fan, Luzhen Tang, Huixiao Le, Kejie Shen, Shufang Tan, Yueying Zhao, Yuan Shen, Xinyu Li, and Dragan Gaˇ sevi´ c. Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance.British Journal of Educational Technology, 56(2):489– 530, 2025

    work page 2025

  42. [42]

    Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

    Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, and Pattie Maes. Your brain on chatgpt: Accumulation of cognitive debt when using an ai assistant for essay writing task.arXiv preprint arXiv:2506.08872, 4, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  43. [43]

    The case for motivated reasoning.Psychological bulletin, 108(3):480–498, 1990

    Ziva Kunda. The case for motivated reasoning.Psychological bulletin, 108(3):480–498, 1990

    work page 1990

  44. [44]

    Biased ai improves human decision-making but reduces trust.arXiv preprint arXiv:2508.09297, 2025

    Shiyang Lai, Junsol Kim, Nadav Kunievsky, Yujin Potter, and James Evans. Biased ai improves human decision-making but reduces trust.arXiv preprint arXiv:2508.09297, 2025

    work page arXiv 2025

  45. [45]

    Junsol Kim, Zhao Wang, Haohan Shi, Hsin-Keng Ling, and James Evans. Differential impact from in- dividual versus collective misinformation tagging on the diversity of twitter (x) information engagement and mobility.Nature Communications, 16(1):973, 2025

    work page 2025

  46. [46]

    Information quality in wikipedia: The effects of group composition and task conflict.Journal of management information systems, 27(4):71–98, 2011

    Ofer Arazy, Oded Nov, Raymond Patterson, and Lisa Yeo. Information quality in wikipedia: The effects of group composition and task conflict.Journal of management information systems, 27(4):71–98, 2011

    work page 2011

  47. [47]

    Dynamics of conflicts in wikipedia.PloS one, 7(6):e38869, 2012

    Taha Yasseri, Robert Sumi, Andr´ as Rung, Andr´ as Kornai, and J´ anos Kert´ esz. Dynamics of conflicts in wikipedia.PloS one, 7(6):e38869, 2012

    work page 2012

  48. [48]

    The wisdom of polarized crowds.Nature human behaviour, 3(4):329–336, 2019

    Feng Shi, Misha Teplitskiy, Eamon Duede, and James A Evans. The wisdom of polarized crowds.Nature human behaviour, 3(4):329–336, 2019

    work page 2019

  49. [49]

    Computational sociology of humans and machines; conflict and collaboration.arXiv preprint arXiv:2412.14606, 2024

    Taha Yasseri. Computational sociology of humans and machines; conflict and collaboration.arXiv preprint arXiv:2412.14606, 2024

    work page arXiv 2024

  50. [50]

    Anonymous cross-party conversations can decrease political polarization: A field experiment on a mobile chat platform.SocArXiv

    Aidan Combs, Graham Tierney, Brian Guay, Friedolin Merhout, Christopher A Bail, D Sunshine Hilly- gus, and Alexander Volfovsky. Anonymous cross-party conversations can decrease political polarization: A field experiment on a mobile chat platform.SocArXiv. September, 23, 2022

    work page 2022

  51. [51]

    Lisa P Argyle, Christopher A Bail, Ethan C Busby, Joshua R Gubler, Thomas Howe, Christopher Ryt- ting, Taylor Sorensen, and David Wingate. Leveraging ai for democratic discourse: Chat interventions can improve online political conversations at scale.Proceedings of the National Academy of Sciences, 120(41):e2311627120, 2023

    work page 2023

  52. [52]

    Interplay between diversity and efficiency in collaborative efforts for content moderation

    Maria Gabriela Juncosa Calahorrano, Saeedeh Mohammadi, Margaret Samahita, and Taha Yasseri. Interplay between diversity and efficiency in collaborative efforts for content moderation. Manuscript in progress, 2024. 23

    work page 2024

  53. [53]

    Collective intelligence in teams: Contex- tualizing collective intelligent behavior over time.Frontiers in psychology, 13:989572, 2022

    Margo Janssens, Nicoleta Meslec, and Roger Th AJ Leenders. Collective intelligence in teams: Contex- tualizing collective intelligent behavior over time.Frontiers in psychology, 13:989572, 2022

    work page 2022

  54. [54]

    The cost of coordination can exceed the benefit of collaboration in performing complex tasks.Collective Intelligence, 2(2):26339137231156912, 2023

    Vincent J Straub, Milena Tsvetkova, and Taha Yasseri. The cost of coordination can exceed the benefit of collaboration in performing complex tasks.Collective Intelligence, 2(2):26339137231156912, 2023

    work page 2023

  55. [55]

    Quantifying collective intelligence in human groups.Proceedings of the National Academy of Sciences, 118(21):e2005737118, 2021

    Christoph Riedl, Young Ji Kim, Pranav Gupta, Thomas W Malone, and Anita Williams Woolley. Quantifying collective intelligence in human groups.Proceedings of the National Academy of Sciences, 118(21):e2005737118, 2021

    work page 2021

  56. [56]

    MIT press, 2015

    Thomas W Malone and Michael Bernstein.Handbook of collective intelligence. MIT press, 2015

    work page 2015

  57. [57]

    Preference for human, not algorithm aversion.Trends in Cognitive Sciences, 26(10):824–826, 2022

    Carey K Morewedge. Preference for human, not algorithm aversion.Trends in Cognitive Sciences, 26(10):824–826, 2022

    work page 2022

  58. [58]

    Testing a new feature to enhance content on tiktok.https://newsroom.tiktok.com/ footnotes, 2025

    Adam Presser. Testing a new feature to enhance content on tiktok.https://newsroom.tiktok.com/ footnotes, 2025. Accessed: 2025-09-24

    work page 2025

  59. [59]

    Community notes reduce the spread of misleading posts on x.OSF Preprint

    Yuwei Chuai, Moritz Pilarski, Gabriele Lenzini, and Nicolas Pr¨ ollochs. Community notes reduce the spread of misleading posts on x.OSF Preprint. https://osf. io/preprints/osf/3a4fe, preprint: not peer reviewed, 2024

    work page 2024

  60. [60]

    Community moderation and the new epistemology of fact checking on social media.arXiv preprint arXiv:2505.20067, 2025

    Isabelle Augenstein, Michiel Bakker, Tanmoy Chakraborty, David Corney, Emilio Ferrara, Iryna Gurevych, Scott Hale, Eduard Hovy, Heng Ji, Irene Larraz, et al. Community moderation and the new epistemology of fact checking on social media.arXiv preprint arXiv:2505.20067, 2025

    work page arXiv 2025

  61. [61]

    Understanding collective intelligence: Investigating the role of collective memory, attention, and reasoning processes.Perspectives on Psychological Science, 19(2):344–354, 2024

    Anita Williams Woolley and Pranav Gupta. Understanding collective intelligence: Investigating the role of collective memory, attention, and reasoning processes.Perspectives on Psychological Science, 19(2):344–354, 2024

    work page 2024

  62. [62]

    Exposure to opposing views on social media can increase political polarization.Proceedings of the National Academy of Sci- ences, 115(37):9216–9221, 2018

    Christopher A Bail, Lisa P Argyle, Taylor W Brown, John P Bumpus, Haohan Chen, MB Fallin Hun- zaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. Exposure to opposing views on social media can increase political polarization.Proceedings of the National Academy of Sci- ences, 115(37):9216–9221, 2018

    work page 2018

  63. [63]

    Hybrid social learning in human-algorithm cultural transmission.Philosophical Transactions of the Royal Society A, 380(2227):20200426, 2022

    Levin Brinkmann, Deniz Gezerli, KV Kleist, Thomas F M¨ uller, Iyad Rahwan, and Niccolo Pescetelli. Hybrid social learning in human-algorithm cultural transmission.Philosophical Transactions of the Royal Society A, 380(2227):20200426, 2022

    work page 2022

  64. [64]

    Crowds can effectively identify misinformation at scale.Perspectives on Psychological Science, 19(2):477–488, 2024

    Cameron Martel, Jennifer Allen, Gordon Pennycook, and David G Rand. Crowds can effectively identify misinformation at scale.Perspectives on Psychological Science, 19(2):477–488, 2024. 24

    work page 2024

  65. [65]

    Mohammed Saeed, Nicolas Traub, Maelle Nicolas, Gianluca Demartini, and Paolo Papotti. Crowd- sourced fact-checking at twitter: how does the crowd compare with experts? InProceedings of the 31st ACM international conference on information & knowledge management, pages 1736–1746, 2022

    work page 2022

  66. [66]

    The rise and decline of an open collaboration system: How wikipedia’s reaction to popularity is causing its decline.American behavioral scientist, 57(5):664–688, 2013

    Aaron Halfaker, R Stuart Geiger, Jonathan T Morgan, and John Riedl. The rise and decline of an open collaboration system: How wikipedia’s reaction to popularity is causing its decline.American behavioral scientist, 57(5):664–688, 2013

    work page 2013

  67. [67]

    Nudging social media toward accuracy.The Annals of the American Academy of Political and Social Science, 700(1):152–164, 2022

    Gordon Pennycook and David G Rand. Nudging social media toward accuracy.The Annals of the American Academy of Political and Social Science, 700(1):152–164, 2022. 25 Supplementary Information for AI Feedback Enhances Community-Based Content Moderation through Engagement with Counterarguments Saeedeh Mohammadi and Taha Yasseri Note Writing Experiment In thi...

    work page 2022