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REVIEW 2 major objections 2 minor 90 references

Understanding and Supporting Online Discussion with Opinionated Chatbots

T0 review · 2 major / 2 minor · reviewed 2026-06-27 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Opinionated chatbots that oppose users produce greater opinion shifts, while reinforcing ones lead to more agreeable later communication.

desk verdict The experiment finds opposing chatbots produce bigger opinion shifts while reinforcing ones improve later communication tone, but the small sample and lab measures leave the real-world claims thin. read the letter →

arxiv 2606.11693 v1 pith:FV5AQMVG submitted 2026-06-10 cs.HC

Tianqi Song , Chi-Lan Yang , Zihan Liu , Zhengtao Xu , Yibin Feng , Yi-Chieh Lee This is my paper · ORCID
classification cs.HC
keywords opinionatedchatbotsonlinediscussionopinionchangecommunicationstylespublicdiscoursechatbotinfluencesocialtopicsusertrust
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

The paper examines how different kinds of opinionated chatbots shape what people think and how they talk afterward in online settings. Participants first discussed a social topic with a chatbot that either opposed their views, reinforced them, or stayed balanced. Those who met opposition revised their opinions more than the others. Those whose views were reinforced later used more agreeable styles when talking with other people. The results point to a practical choice for designers who want chatbots to support open discussion without harming trust.

What carries the argument

Three types of opinionated chatbots (opposing, reinforcing, or balanced) that users encounter before they join online discussions with other people.

What would settle it

A follow-up study on a live discussion platform where users who first chat with opposing bots show no larger opinion shifts than users who chat with reinforcing bots would falsify the central result.

Watch

Extended reading notes

Core claim

In a controlled experiment with 83 participants, interacting with an opinionated chatbot that consistently opposed participants' arguments led to greater shifts in opinion, indicating enhanced openness to revising one's initial stance. Conversely, participants who interacted with a chatbot that consistently reinforced their views were more likely to adopt more agreeable communication styles in subsequent conversations with others. Interactions with different types of opinionated chatbots resulted in varying levels of trust as well as different perceptions of chatbots and human interlocutors.

Load-bearing premise

The lab experiment with 83 participants captures the real causal effects of chatbot type on opinion change and communication style without the artificial setting or measures introducing bias.

Editorial extensions

If this is right

  • Opinionated chatbots can change how open people are to revising their views on social topics.
  • Reinforcing chatbots encourage more agreeable styles when users later talk with others.
  • Different chatbot types produce different levels of user trust and different perceptions of both bots and humans.
  • Designers of such chatbots face a trade-off between promoting opinion flexibility and preserving positive experiences and trust.

Reading between the lines

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

  • Platforms could test these bots as a way to reduce entrenched positions before users join group threads.
  • The pattern might appear with other conversational AI tools that users meet before public exchanges.
  • Longer-term studies on actual forums would show whether the lab effects persist once users return to their normal online habits.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript reports results from a controlled lab experiment (N=83) on the effects of three types of opinionated chatbots (opposing, reinforcing, balanced) on participants' subsequent opinion change, communication style in online discussions, trust levels, and perceptions of chatbots versus humans. It claims that opposing chatbots produce greater opinion shifts while reinforcing chatbots increase agreeable communication styles, with varying trust and perception outcomes, and discusses design trade-offs for reducing polarization.

Significance. If the experimental results are robust and generalize, the work identifies a concrete trade-off in chatbot design: opposing stances may increase cognitive flexibility at the cost of trust, while reinforcing stances preserve positive interactions but may entrench views. This has direct implications for platform interventions aimed at constructive online discourse.

major comments (2)
  1. [Abstract] Abstract and Methods (implied section): The abstract states directional findings from the N=83 experiment but supplies no statistical details (p-values, effect sizes, confidence intervals), sample characteristics beyond N=83, measurement instruments, exclusion criteria, or analysis methods, leaving the central causal claims without verifiable support in the reported text.
  2. [Discussion] Discussion section: The inference that lab-induced opinion shifts and communication-style changes will affect real-world online discussion behavior rests on an untested assumption of ecological validity; the controlled setting, scripted chatbot interactions, and post-task discussion measures may introduce demand characteristics or response constraints absent from naturalistic platforms.
minor comments (2)
  1. [Abstract] The final sentence of the abstract is truncated ('less polarized online').
  2. [Methods] The manuscript should include a CONSORT-style flow diagram or explicit participant recruitment and exclusion details to allow assessment of selection bias.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment below, providing clarifications and indicating where revisions will be made to strengthen the paper.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Methods (implied section): The abstract states directional findings from the N=83 experiment but supplies no statistical details (p-values, effect sizes, confidence intervals), sample characteristics beyond N=83, measurement instruments, exclusion criteria, or analysis methods, leaving the central causal claims without verifiable support in the reported text.

    Authors: We agree that the abstract is highly concise and omits detailed statistical reporting, which is a common constraint due to word limits but can reduce immediate verifiability. The full Methods and Results sections contain the requested details (e.g., pre-registered analysis plan, Likert-scale instruments, exclusion criteria based on attention checks, and effect sizes). We will revise the abstract to incorporate key statistical highlights such as main effect p-values and Cohen's d where feasible without exceeding length limits. revision: partial

  2. Referee: [Discussion] Discussion section: The inference that lab-induced opinion shifts and communication-style changes will affect real-world online discussion behavior rests on an untested assumption of ecological validity; the controlled setting, scripted chatbot interactions, and post-task discussion measures may introduce demand characteristics or response constraints absent from naturalistic platforms.

    Authors: We acknowledge the inherent limits of lab experiments for ecological validity and do not claim direct real-world generalization. The controlled design was chosen specifically to establish causal effects of chatbot stance on opinion change and communication style, isolating variables that would be confounded in field settings. The manuscript already includes a dedicated Limitations subsection discussing demand characteristics, scripted interactions, and the need for future field studies. We will expand this subsection with additional caveats on generalizability but maintain that the internal-validity findings remain valuable for informing design trade-offs. revision: no

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical user study with direct experimental measurements

full rationale

This is a controlled lab experiment (N=83) reporting observed effects of chatbot types on opinion shifts and communication styles. No equations, model derivations, fitted parameters renamed as predictions, or self-citation load-bearing steps exist. All claims rest on direct participant data collection and statistical comparison rather than any reduction to inputs by construction. The paper contains no theoretical chain that could exhibit self-definitional, fitted-input, or ansatz-smuggling circularity.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The paper is an empirical HCI user study. It rests on standard experimental assumptions rather than new mathematical constructs or invented entities.

assumptions (1)
  • domain assumption The chatbots were successfully implemented to express the intended opposing, reinforcing, or balanced viewpoints without unintended bias in phrasing or consistency.
    The validity of the reported differences depends on the bots actually delivering the three distinct stance conditions as described.

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Cite this review

Pith. "Pith review of Understanding and Supporting Online Discussion with Opinionated Chatbots." pith.science (2026). https://pith.science/paper/FV5AQMVG

@misc{pith2026260611693,
  author       = {Pith},
  title        = {Pith review of: Understanding and Supporting Online Discussion with Opinionated Chatbots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FV5AQMVG}},
  note         = {Machine review of arXiv:2606.11693}
}
read the original abstract

Opinionated chatbots are increasingly present on online platforms and have the potential to shape public discourse by influencing individuals' viewpoints before they engage in discussions. Despite their growing presence, the impact of interacting with opinionated chatbots on subsequent online interactions remains largely unexplored. This study investigated how exposure to different types of opinionated chatbots, specifically those expressing opposing, reinforcing, or balanced viewpoints, affected participants' subsequent online discussions. In a controlled experiment with 83 participants, we found that interacting with an opinionated chatbot that consistently opposed participants' arguments led to greater shifts in opinion, indicating enhanced openness to revising one's initial stance. Conversely, participants who interacted with a chatbot that consistently reinforced their views were more likely to adopt more agreeable communication styles in subsequent conversations with others. Furthermore, interactions with different types of opinionated chatbots resulted in varying levels of trust, as well as different perceptions of chatbots and human interlocutors. Our findings indicate that opinionated chatbots can influence both individuals' opinions on social topics and their communication behaviors in online environments. This presents a trade-off for future designers seeking to facilitate cognitive flexibility in changing opinions while maintaining positive user experiences and trust in the chatbots during public discourse. We discuss the implications for designing opinionated chatbots to promote more constructive and less polarized online

Figures

Figures reproduced from arXiv: 2606.11693 by the authors.

Figure 1
Figure 1. The conversation design comprises two parts: (a) the participant’s interaction with the chatbot and [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Illustrative example of the chatting interface from the participants’ perspectives: (a) interaction with a [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. The study procedure includes four parts: (1) Topic Introduction. A research assistant introduces the [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: %(Opinion Shift) After Discussing with Chatbots. Bar chart illustrating the ratio of participants whose [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: %(Communication Patterns) Between Participants and Interlocutors. Bar chart showing the ratio [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Perceived Trust (left) and Communication Quality (right). Box plots showing the measured values of [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

90 extracted references · 2 canonical work pages

  1. [1]

    Gordon Willard Allport, Kenneth Clark, and Thomas Pettigrew. 1954. The nature of prejudice. (1954)

    1954

  2. [2]

    Lisa P Argyle, Christopher A Bail, Ethan C Busby, Joshua R Gubler, Thomas Howe, Christopher Rytting, Taylor Sorensen, and David Wingate. 2023. Leveraging AI for democratic discourse: Chat interventions can improve online political conversations at scale.Proceedings of the National Academy of Sciences120, 41 (2023), e2311627120

    2023

  3. [3]

    Christopher A Bail, Lisa P Argyle, Taylor W Brown, John P Bumpus, Haohan Chen, MB Fallin Hunzaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. 2018. Exposure to opposing views on social media can increase political polarization.Proceedings of the National Academy of Sciences115, 37 (2018), 9216–9221

    2018

  4. [4]

    Stefano Balietti, Lise Getoor, Daniel G Goldstein, and Duncan J Watts. 2021. Reducing opinion polarization: Effects of exposure to similar people with differing political views.Proceedings of the National Academy of Sciences118, 52 (2021), e2112552118

    2021

  5. [5]

    Albert Bandura, Dorothea Ross, and Sheila A Ross. 1961. Transmission of aggression through imitation of aggressive models.The Journal of Abnormal and Social Psychology63, 3 (1961), 575

    1961

  6. [6]

    Albert Bandura, Dorothea Ross, and Sheila A Ross. 1963. Imitation of film-mediated aggressive models.The Journal of Abnormal and Social Psychology66, 1 (1963), 3

    1963

  7. [7]

    1977.Social learning theory

    Albert Bandura and Richard H Walters. 1977.Social learning theory. Vol. 1. Prentice hall Englewood Cliffs, NJ

    1977

  8. [8]

    Amanda Baughan, Justin Petelka, Catherine Jaekyung Yoo, Jack Lo, Shiyue Wang, Amulya Paramasivam, Ashley Zhou, and Alexis Hiniker. 2021. Someone is wrong on the internet: Having hard conversations in online spaces.Proceedings of the ACM on Human-Computer Interaction5, CSCW1 (2021), 1–22

    2021

Show all 90 references
  1. [9]

    2009.Media effects: Advances in theory and research

    Jennings Bryant and Mary Beth Oliver. 2009.Media effects: Advances in theory and research. Routledge

    2009

  2. [10]

    Inna Čábelková, Luboš Smutka, David Mareš, Akhmadjon Ortikov, and Stanislava Kontsevaya. 2023. Environmental protection or economic growth? The effects of preferences for individual freedoms.Frontiers in Environmental Science 11 (2023), 1129236

    2023

  3. [11]

    Yan Chen and Sherry Xin Li. 2009. Group identity and social preferences.American Economic Review99, 1 (2009), 431–457

    2009

  4. [12]

    Chun-Wei Chiang, Zhuoran Lu, Zhuoyan Li, and Ming Yin. 2024. Enhancing AI-Assisted Group Decision Making through LLM-Powered Devil’s Advocate. InProceedings of the 29th International Conference on Intelligent User Interfaces. 103–119

    2024

  5. [13]

    Robert B Cialdini and Noah J Goldstein. 2004. Social influence: Compliance and conformity.Annu. Rev. Psychol.55, 1 (2004), 591–621

    2004

  6. [14]

    Giusy Cirillo, Elin Runnqvist, Kristof Strijkers, Noël Nguyen, and Cristina Baus. 2022. Conceptual alignment in a joint picture-naming task performed with a social robot.Cognition227 (2022), 105213

    2022

  7. [15]

    Aidan Combs, Graham Tierney, Brian Guay, Friedolin Merhout, Christopher A Bail, D Sunshine Hillygus, and Alexander Volfovsky. 2023. Reducing political polarization in the United States with a mobile chat platform.Nature human Proc. ACM Hum.-Comput. Interact., Vol. 10, No. 6, A...

    2023

  8. [16]

    Scott A Crossley. 2020. Linguistic features in writing quality and development: An overview.Journal of Writing Research11, 3 (2020), 415–443

    2020

  9. [17]

    Morton Deutsch and Harold B Gerard. 1955. A study of normative and informational social influences upon individual judgment.The journal of abnormal and social psychology51, 3 (1955), 629

    1955

  10. [18]

    Nicholas Diakopoulos and Mor Naaman. 2011. Towards quality discourse in online news comments. InProceedings of the ACM 2011 conference on Computer supported cooperative work. 133–142

    2011

  11. [19]

    Tilman Dingler, Ashris Choudhury, and Vassilis Kostakos. 2018. Biased bots: Conversational agents to overcome polarization. InProceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computer...

    2018

  12. [20]

    2016.Social influence: Direct and indirect processes

    Joseph P Forgas and Kipling D Williams. 2016.Social influence: Direct and indirect processes. Psychology Press

    2016

  13. [21]

    Catherine M Gayman and Stephanie T Jimenez. 2020. Varying the preparation guide and group discussion in a classroom analysis of interteaching.Journal of the Scholarship of Teaching and Learning20, 2 (2020)

    2020

  14. [22]

    Shixian Geng, Remi Inayoshi, Chi-Lan Yang, Zefan Sramek, Yuya Umeda, Chiaki Kasahara, Arissa J Sato, Simo Hosio, and Koji Yatani. 2025. Beyond the Dialogue: Multi-chatbot Group Motivational Interviewing for Premenstrual Syndrome (PMS) Management. InProceedings of the 2025 CHI ...

    2025

  15. [23]

    Edward Gibson. 1998. Linguistic complexity: Locality of syntactic dependencies.Cognition68, 1 (1998), 1–76

    1998

  16. [24]

    Howard Giles, Nikolas Coupland, and Justine Coupland. 1991. Accommodation theory: Communication, context, and consequence.Contexts of accommodation: Developments in applied sociolinguistics1 (1991), 1–68

    1991

  17. [25]

    Howard Giles, America L Edwards, and Joseph B Walther. 2023. Communication accommodation theory: Past accomplishments, current trends, and future prospects.Language Sciences99 (2023), 101571

    2023

  18. [26]

    Howard Giles, Tania Ogay, et al. 2007. Communication accommodation theory. (2007)

    2007

  19. [27]

    Rafik Hadfi, Jawad Haqbeen, Sofia Sahab, and Takayuki Ito. 2021. Argumentative conversational agents for online discussions.Journal of Systems Science and Systems Engineering30 (2021), 450–464

    2021

  20. [28]

    Rafik Hadfi, Shun Okuhara, Jawad Haqbeen, Sofia Sahab, Susumu Ohnuma, and Takayuki Ito. 2023. Conversational agents enhance women’s contribution in online debates.Scientific Reports13, 1 (2023), 14534

    2023

  21. [29]

    Peter R Harris, Elizabeth Sillence, and Pam Briggs. 2011. Perceived threat and corroboration: key factors that improve a predictive model of trust in internet-based health information and advice.Journal of medical Internet research13, 3 (2011), e51

    2011

  22. [30]

    Elaine Hatfield, John T Cacioppo, and Richard L Rapson. 1993. Emotional contagion.Current directions in psychological science2, 3 (1993), 96–100

    1993

  23. [31]

    Staffan Himmelroos and Henrik Serup Christensen. 2014. Deliberation and Opinion Change: Evidence from a Deliberative Mini-public in F inland.Scandinavian Political Studies37, 1 (2014), 41–60

    2014

  24. [32]

    Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2023. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Infor...

    2023

  25. [33]

    Leonie Huddy. 2001. From social to political identity: A critical examination of social identity theory.Political Psychology22, 1 (2001), 127–156

    2001

  26. [34]

    Carolin Ischen, Janice Butler, and Jakob Ohme. 2024. Chatting about the unaccepted: Self-disclosure of unaccepted news exposure behaviour to a chatbot.Behaviour & Information Technology43, 10 (2024), 2044–2056

    2024

  27. [35]

    Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, and Mor Naaman. 2023. Co-writing with opinionated language models affects users’ views. InProceedings of the 2023 CHI conference on human factors in computing systems. 1–15

    2023

  28. [36]

    Andreas Janson. 2023. How to leverage anthropomorphism for chatbot service interfaces: The interplay of communi- cation style and personification.Computers in Human Behavior149 (2023), 107954

    2023

  29. [37]

    Zhiqiu Jiang, Mashrur Rashik, Kunjal Panchal, Mahmood Jasim, Ali Sarvghad, Pari Riahi, Erica DeWitt, Fey Thurber, and Narges Mahyar. 2023. CommunityBots: creating and evaluating A multi-agent chatbot platform for public input elicitation.Proceedings of the ACM on Human-Compute...

    2023

  30. [38]

    Soomin Kim, Jinsu Eun, Changhoon Oh, Bongwon Suh, and Joonhwan Lee. 2020. Bot in the bunch: Facilitating group chat discussion by improving efficiency and participation with a chatbot. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13

    2020

  31. [39]

    Soomin Kim, Jinsu Eun, Joseph Seering, and Joonhwan Lee. 2021. Moderator chatbot for deliberative discussion: Effects of discussion structure and discussant facilitation.Proceedings of the ACM on Human-Computer Interaction5, CSCW1 (2021), 1–26

    2021

  32. [40]

    Silvia Knobloch-Westerwick and Jingbo Meng. 2009. Looking the other way: Selective exposure to attitude-consistent and counterattitudinal political information.Communication Research36, 3 (2009), 426–448. Proc. ACM Hum.-Comput. Interact., Vol. 10, No. 6, Article CSCW176. Publi...

    2009

  33. [41]

    K Hazel Kwon and Daegon Cho. 2017. Swearing effects on citizen-to-citizen commenting online: A large-scale exploration of political versus nonpolitical online news sites.Social Science Computer Review35, 1 (2017), 84–102

    2017

  34. [42]

    Lasha Labadze, Maya Grigolia, and Lela Machaidze. 2023. Role of AI chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education20, 1 (2023), 56

    2023

  35. [43]

    Soohwan Lee, Seoyeong Hwang, Dajung Kim, and Kyungho Lee. 2025. Conversational Agents as Catalysts for Critical Thinking: Challenging Social Influence in Group Decision-making. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems ...

    2025 doi

  36. [44]

    SooHwan Lee, Mingyu Kim, Seoyeong Hwang, Dajung Kim, and Kyungho Lee. 2025. Amplifying Minority Voices: AI-Mediated Devil’s Advocate System for Inclusive Group Decision-Making. InCompanion Proceedings of the 30th International Conference on Intelligent User Interfaces. 17–21

    2025

  37. [45]

    Sung-Chul Lee, Jaeyoon Song, Eun-Young Ko, Seongho Park, Jihee Kim, and Juho Kim. 2020. Solutionchat: Real-time moderator support for chat-based structured discussion. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–12

    2020

  38. [46]

    Unggi Lee, Yeil Jeong, Junbo Koh, Gyuri Byun, Yunseo Lee, Youngsun Hwang, Hyeoncheol Kim, and Cheolil Lim. 2024. Can ChatGPT be a debate partner? Developing ChatGPT-based application “DEBO” for debate education, findings and limitations.Educational Technology & Society27, 2 (2...

    2024

  39. [47]

    Jingshu Li, Tianqi Song, Beichen Xue, and Yi-Chieh Lee. 2025. We Shape AI, and Thereafter AI Shape Us: Humans Align with AI through Social Influences. InICLR 2025 Workshop on Bidirectional Human-AI Alignment

    2025

  40. [48]

    Leigh Anne Liu, Chei Hwee Chua, and Günter K Stahl. 2010. Quality of communication experience: Definition, measurement, and implications for intercultural negotiations.Journal of Applied Psychology95, 3 (2010), 469

    2010

  41. [49]

    Charles G Lord, Lee Ross, and Mark R Lepper. 1979. Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence.Journal of personality and social psychology37, 11 (1979), 2098

    1979

  42. [50]

    Phaedra S Mohammed and Eleanor ‘Nell’Watson. 2019. Towards inclusive education in the age of artificial intelligence: Perspectives, challenges, and opportunities.Artificial Intelligence and Inclusive Education: Speculative futures and emerging practices(2019), 17–37

    2019

  43. [51]

    2006.Hearing the other side: Deliberative versus participatory democracy

    Diana C Mutz. 2006.Hearing the other side: Deliberative versus participatory democracy. Cambridge University Press

    2006

  44. [52]

    Abdullah Bin Nofal, Hassan Ali, Muhammad Hadi, Aizaz Ahmad, Adnan Qayyum, Aditya Johri, Ala Al-Fuqaha, and Junaid Qadir. 2025. AI-enhanced interview simulation in the metaverse: Transforming professional skills training through VR and generative conversational AI.Computers and...

    2025

  45. [53]

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems35 ...

    2022

  46. [54]

    Bogyeom Park and Kyoungwon Seo. 2025. Assessing Critical Thinking through a Multi-Agent LLM-Based Debate Chatbot. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–13

    2025

  47. [55]

    Louise F Pendry and Jessica Salvatore. 2015. Individual and social benefits of online discussion forums.Computers in Human Behavior50 (2015), 211–220

    2015

  48. [56]

    James W Pennebaker, Martha E Francis, and Roger J Booth. 2001. Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates71, 2001 (2001), 2001

    2001

  49. [57]

    Vincent Price, Joseph N Cappella, and Lilach Nir. 2002. Does disagreement contribute to more deliberative opinion? Political communication19, 1 (2002), 95–112

    2002

  50. [58]

    2022.The Smiling Chatbot: Investigating Emotional Contagion in Human-to-Chatbot Service Interactions

    Konstantin Prinz. 2022.The Smiling Chatbot: Investigating Emotional Contagion in Human-to-Chatbot Service Interactions. Springer Nature

    2022

  51. [59]

    Alex Reinhart, Ben Markey, Michael Laudenbach, Kachatad Pantusen, Ronald Yurko, Gordon Weinberg, and David West Brown. 2025. Do LLMs write like humans? Variation in grammatical and rhetorical styles.Proceedings of the National Academy of Sciences122, 8 (2025), e2422455122

    2025

  52. [60]

    Paolo Riva, Nicolas Aureli, and Federica Silvestrini. 2022. Social influences in the digital era: When do people conform more to a human being or an artificial intelligence?Acta psychologica229 (2022), 103681

    2022

  53. [61]

    Sofia Sahab, Jawad Haqbeen, Rafik Hadfi, Takayuki Ito, Richard Eke Imade, Susumu Ohnuma, and Takuya Hasegawa

  54. [62]

    E-contact facilitated by conversational agents reduces interethnic prejudice and anxiety in Afghanistan.Com- munications Psychology2, 1 (2024), 22

    2024

  55. [63]

    Nikhil Sharma, Q Vera Liao, and Ziang Xiao. 2024. Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking. InProceedings of the CHI Conference on Human Factors in Computing Systems. 1–17

    2024

  56. [64]

    Huiyang Shen and Min Wang. 2023. Effects of social skills on lexical alignment in human-human interaction and human-computer interaction.Computers in Human Behavior143 (2023), 107718

    2023

  57. [65]

    Ruth Shortall, Anatol Itten, Michiel van der Meer, Pradeep Murukannaiah, and Catholijn Jonker. 2022. Reason against the machine? Future directions for mass online deliberation.Frontiers in Political Science4 (2022), 946589. Proc. ACM Hum.-Comput. Interact., Vol. 10, No. 6, Art...

    2022

  58. [66]

    Tianqi Song, Jack Jamieson, Tianwen Zhu, Naomi Yamashita, and Yi-Chieh Lee. 2025. From Interaction to Attitude: Exploring the Impact of Human-AI Cooperation on Mental Illness Stigma.Proceedings of the ACM on Human-Computer Interaction9, 2 (2025), 1–31

    2025

  59. [67]

    Tianqi Song, Yugin Tan, Zicheng Zhu, Yibin Feng, and Yi-Chieh Lee. 2025. Greater than the Sum of its Parts: Exploring Social Influence of Multi-Agents. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–11

    2025

  60. [68]

    Tianqi Song, Yugin Tan, Zicheng Zhu, Yibin Feng, and Yi-Chieh Lee. 2025. Multi-agents are social groups: Investigating social influence of multiple agents in human-agent interactions.Proceedings of the ACM on Human-Computer Interaction 9, 7 (2025), 1–33

    2025

  61. [69]

    Sofia Eleni Spatharioti, David M Rothschild, Daniel G Goldstein, and Jake M Hofman. 2023. Comparing traditional and llm-based search for consumer choice: A randomized experiment.arXiv preprint arXiv:2307.03744(2023)

    2023

  62. [70]

    John Suler. 2004. The online disinhibition effect.Cyberpsychology & behavior7, 3 (2004), 321–326

    2004

  63. [71]

    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. InProceedings of the CHI Conference on Human Factors in Computing Systems. 1–24

    2024

  64. [72]

    Petter Törnberg. 2022. How digital media drive affective polarization through partisan sorting.Proceedings of the National Academy of Sciences119, 42 (2022), e2207159119

    2022

  65. [73]

    Joseph B Walther and Ulla Bunz. 2005. The rules of virtual groups: Trust, liking, and performance in computer-mediated communication.Journal of communication55, 4 (2005), 828–846

    2005

  66. [74]

    Thiemo Wambsganss, Andreas Janson, and Jan Marco Leimeister. 2022. Enhancing argumentative writing with automated feedback and social comparison nudging.Computers & Education191 (2022), 104644

    2022

  67. [75]

    Thiemo Wambsganss, Tobias Kueng, Matthias Soellner, and Jan Marco Leimeister. 2021. ArgueTutor: An adaptive dialog-based learning system for argumentation skills. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–13

    2021

  68. [76]

    Florian Weber, Thiemo Wambsganss, Seyed Parsa Neshaei, and Matthias Soellner. 2024. LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law Students. InProceedings of the CHI Conference on Human Factors in Computing Sy...

    2024

  69. [77]

    Magdalena Wojcieszak and Benjamin R Warner. 2020. Can interparty contact reduce affective polarization? A systematic test of different forms of intergroup contact.Political Communication37, 6 (2020), 789–811

    2020

  70. [78]

    Zehang Xie, Shuoshuo Li, and Wu Li. 2025. Do discussions in human-computer communities trigger group polarization? Insights from the media evocation paradigm.Computers in Human Behavior165 (2025), 108561

    2025

  71. [79]

    Ningyu Xu, Qi Zhang, Chao Du, Qiang Luo, Xipeng Qiu, Xuanjing Huang, and Menghan Zhang. 2025. Revealing emergent human-like conceptual representations from language prediction.Proceedings of the National Academy of Sciences122, 44 (2025), e2512514122

    2025

  72. [80]

    Brahim Zarouali, Mykola Makhortykh, Mariella Bastian, and Theo Araujo. 2021. Overcoming polarization with chatbot news? Investigating the impact of news content containing opposing views on agreement and credibility.European journal of communication36, 1 (2021), 53–68

    2021

  73. [81]

    " Ronaldo’s a poser!

    Yuhan Zeng, Yingxuan Shi, Xuehan Huang, Fiona Nah, and RAY LC. 2025. "" Ronaldo’s a poser!"": How the Use of Generative AI Shapes Debates in Online Forums. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–22

    2025

  74. [82]

    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. InProceedings of the 2024 CHI Conference on Human Factors in Co...

    2024

  75. [83]

    Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018. Emotional chatting machine: Emotional conversation generation with internal and external memory. InProceedings of the AAAI conference on artificial intelligence, Vol. 32

    2018

  76. [84]

    1991.The psychology of attitude change and social influence.Mcgraw-Hill Book Company

    Philip G Zimbardo and Michael R Leippe. 1991.The psychology of attitude change and social influence.Mcgraw-Hill Book Company. A Appendix A.1 Survey Items A.1.1 Trust. •The advice from the chatbot seemed impartial and independent. •The advice from the chatbot seemed objective (...

    1991

  77. [85]

    Give one argument that supports their view

  78. [86]

    Style: Your tone should be neutral and conversational, simulating a spoken discussion

    Give one argument that counters their view. Style: Your tone should be neutral and conversational, simulating a spoken discussion. Remember to keep your answer short. Note: After sharing your arguments, always ask the user their thoughts about your response, such as: - ’What d...

  79. [87]

    Alwasy disagree with their statement

  80. [88]

    Style: Your tone should remain respectful but firm, simulating a spoken discussion

    Give one concise argument to oppose their view. Style: Your tone should remain respectful but firm, simulating a spoken discussion. Remember to keep your answer short. Note: After sharing your arguments, always ask the user their thoughts about your response, such as: - ’What ...

    2026

  81. [89]

    Alwasy agree with their statement

  82. [90]

    Part 1 BOT

    Give one concise argument that supplements their view. Style: Your tone should be positive and conversational, simulating a spoken discussion. Remember to keep your answer short. Note: After sharing your arguments, always ask the user their thoughts about your response, such a...

    2026

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.