pith. machine review for the scientific record. sign in

arxiv: 2605.02842 · v2 · submitted 2026-05-04 · 💻 cs.HC

Recognition: 2 theorem links

· Lean Theorem

"I Don't Have Faith in the Developers to Use My Feedback": Understanding Player Values and Expectancy for Reporting Systems in Video Games

Michael Yin , Chenxinran (Elise) Shen , Robert Xiao
Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:54 UTC · model grok-4.3

classification 💻 cs.HC
keywords video gamesreporting systemsplayer motivationsonline toxicitymoderationexpectancy-value theoryaltruismretribution
0
0 comments X

The pith

Players report toxic behavior in video games for both short-term revenge and long-term community improvement, but their trust in the system depends on developer reputation, transparency, and community alignment.

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

This paper applies expectancy-value theory to examine why players use reporting systems in multiplayer video games. Players place value on reporting because it can deliver immediate punishment to offenders and support a healthier community over time. Their expectation that reports will actually produce these results is shaped by how much they trust the developers to act, how visible the process and outcomes are, and whether the system matches what the community expects. Low expectations mean that even valued outcomes may not lead to actual reporting. These patterns matter for designing moderation tools that players will actually use to address toxicity.

Core claim

Reporting is motivated by both altruistic and retributive factors, with players seeking short-term revenge while also looking to foster an improved long-term community. Yet, players felt that reporting may not always meet these goals, with belief in the system being mediated by factors such as developer reputation, reporting transparency, and alignment with the community.

What carries the argument

Expectancy-value theory applied to reporting, separating the worth players assign to outcomes like punishment and community health from their confidence that the reporting system will deliver those outcomes.

If this is right

  • Players will report toxic behavior less often when they doubt developers will act on the feedback.
  • Making report outcomes more visible to players can raise expectations and increase reporting activity.
  • Reporting systems that reflect community standards are more likely to be used than those that do not.
  • A developer's poor reputation can reduce the effectiveness of even technically sound reporting tools.

Where Pith is reading between the lines

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

  • The same split between short-term and long-term motivations could explain participation in moderation on social media or forums.
  • Developers could run experiments that vary transparency levels and measure changes in reporting volume to test the expectancy link directly.
  • Persistent low expectancy might lead players to avoid multiplayer modes or switch games rather than continue reporting issues.

Load-bearing premise

The small sample of 98 survey respondents and 19 interviewees recruited through distributed channels sufficiently captures the diversity of player values and expectations across games and demographics without significant selection bias.

What would settle it

A larger study across multiple games that finds no relationship between players' stated trust in developers and their actual reporting rates would undermine the claim that developer reputation mediates expectancy.

Figures

Figures reproduced from arXiv: 2605.02842 by Chenxinran (Elise) Shen, Michael Yin, Robert Xiao.

Figure 1
Figure 1. Figure 1: Number of questionnaire respondents that have reported and would report for specific reasons (out of view at source ↗
Figure 2
Figure 2. Figure 2: Number of questionnaire respondents that agreed with how much each factor affected trust (out of 98 view at source ↗
read the original abstract

Reporting systems in multiplayer video games allow players to express their dissatisfaction with others and combat in-game toxicity. In this work, we examined the act of reporting through the lens of expectancy-value theory. Using a distributed survey (n = 98) and follow-up interviews (n = 19), we explored the value players place on reporting, their desired outcomes, and their expectations that these outcomes will be achieved. Our findings revealed that reporting is motivated by both altruistic and retributive factors, with players seeking short-term revenge while also looking to foster an improved long-term community. Yet, players felt that reporting may not always meet these goals, with belief in the system being mediated by factors such as developer reputation, reporting transparency, and alignment with the community. By understanding the value and expectancy of reporting systems, we discuss their implications on broader digital moderation and consider current and potential future designs of reporting systems.

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 / 2 minor

Summary. The manuscript applies expectancy-value theory to examine player motivations for using in-game reporting systems in multiplayer video games. Drawing on a distributed survey (n=98) and follow-up interviews (n=19), the authors identify both altruistic (long-term community improvement) and retributive (short-term revenge) values driving reporting behavior, while noting that players' expectations of system efficacy are mediated by developer reputation, reporting transparency, and perceived community alignment. The work discusses implications for digital moderation and potential improvements to reporting system design.

Significance. If the descriptive findings hold for the sampled population, the study contributes to HCI and game studies by providing a theoretically grounded account of user expectations for moderation tools. It highlights trust-related barriers that could guide more effective reporting mechanisms, with potential relevance to broader online community governance. The mixed-methods approach yields concrete themes that designers could use to address gaps between player goals and system outcomes.

major comments (2)
  1. [Methods] Methods section (participant recruitment and sample description): The distributed recruitment via social media and forums, yielding n=98 survey responses and n=19 interviews, is presented without sufficient detail on screening, demographics, or steps taken to mitigate selection bias toward engaged, English-speaking, or Western players. Since the central claims about altruistic/retributive motivations and the three mediating factors rest entirely on thematic analysis of this sample, the absence of explicit bias assessment or representativeness discussion weakens the applicability of the reported values and expectancies beyond the obtained group.
  2. [Findings/Discussion] Findings and Discussion sections: The linkage between specific interview quotes/themes and the expectancy-value theory constructs (value components vs. expectancy mediators) is not always explicit, making it difficult to assess how the data directly support the claim that belief in the system is mediated by developer reputation, transparency, and community alignment rather than other unexamined factors.
minor comments (2)
  1. [Abstract] Abstract: The phrasing 'distributed survey' could be clarified to indicate it was an online instrument, consistent with the methods description.
  2. [Related Work] Related Work: A few citations on online toxicity and moderation (e.g., recent CHI or CSCW papers on reporting efficacy) appear missing, which would better situate the expectancy-value application.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. The comments identify valuable opportunities to improve methodological transparency and the explicit integration of our data with expectancy-value theory. We address each major comment below and will incorporate revisions in the next version of the manuscript.

read point-by-point responses

  1. Referee: [Methods] Methods section (participant recruitment and sample description): The distributed recruitment via social media and forums, yielding n=98 survey responses and n=19 interviews, is presented without sufficient detail on screening, demographics, or steps taken to mitigate selection bias toward engaged, English-speaking, or Western players. Since the central claims about altruistic/retributive motivations and the three mediating factors rest entirely on thematic analysis of this sample, the absence of explicit bias assessment or representativeness discussion weakens the applicability of the reported values and expectancies beyond the obtained group.

    Authors: We agree that greater detail on recruitment and an explicit discussion of limitations would strengthen the paper. In the revised manuscript we will expand the Methods section to describe the specific social media channels and forums used, any screening criteria applied to ensure participants had recent experience with multiplayer reporting systems, and the demographic data collected (age, gender, gaming frequency, and region where available). We will also add a dedicated limitations subsection that directly addresses selection bias, the English-speaking/Western skew, and the exploratory nature of the sample, while clarifying that the study does not claim population-level representativeness. These additions will better contextualize the scope of the reported values and expectancies. revision: yes

  2. Referee: [Findings/Discussion] Findings and Discussion sections: The linkage between specific interview quotes/themes and the expectancy-value theory constructs (value components vs. expectancy mediators) is not always explicit, making it difficult to assess how the data directly support the claim that belief in the system is mediated by developer reputation, transparency, and community alignment rather than other unexamined factors.

    Authors: We appreciate the referee’s call for clearer theoretical mapping. In the revised Findings section we will reorganize the presentation to explicitly label each major theme according to the EVT constructs: altruistic and retributive value components will be distinguished with supporting quotes, and the three expectancy mediators (developer reputation, reporting transparency, and community alignment) will be tied directly to the data through additional cross-references and brief explanatory sentences. A new summary table will list representative quotes alongside the specific EVT element they illustrate. This will make the evidential basis for the mediators more transparent while still acknowledging that other unexamined factors may exist; the revisions will show how the collected data support the three mediators as salient in this sample. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical qualitative study derives claims directly from collected data

full rationale

The paper is a self-contained empirical study using distributed surveys (n=98) and interviews (n=19) with thematic analysis under expectancy-value theory. No mathematical derivations, equations, fitted parameters, or model-based predictions exist. Claims about altruistic/retributive motivations and mediating factors (developer reputation, transparency, community alignment) are presented as arising from participant responses rather than reducing to self-citations, definitional loops, or renamed inputs. Self-citations, if present, are not load-bearing for the central findings, which remain externally falsifiable via the described data collection process.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the applicability of expectancy-value theory to this domain and the representativeness of the participant sample. No free parameters, invented entities, or ad-hoc axioms beyond standard qualitative research assumptions.

axioms (2)
  • domain assumption Expectancy-value theory provides a valid lens for interpreting player reporting behavior
    Invoked in the abstract to frame the study design and findings
  • domain assumption Self-reported survey and interview data accurately reflect players' true values and expectations
    Implicit in all qualitative findings from n=98 and n=19

pith-pipeline@v0.9.0 · 5466 in / 1298 out tokens · 34792 ms · 2026-05-08T17:54:20.249343+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

95 extracted references · 56 canonical work pages

  1. [1]

    Leigh Achterbosch, Charlynn Miller, and Peter Vamplew. 2017. A Taxonomy of Griefer Type by Motivation in Massively Multiplayer Online Role-Playing Games.Behaviour & Information Technology36, 8 (Aug. 2017), 846–860. https://doi.org/10.1080/0144929X.2017.1306109

    work page doi:10.1080/0144929x.2017.1306109 2017

  2. [2]

    Leigh Achterbosch, Peter Vamplew, and Evita March. 2024. Assessing the Impact of Griefing in MMORPGs Using Self- Determination Theory.Computers in Human Behavior161 (Dec. 2024), 108388. https://doi.org/10.1016/j.chb.2024.108388

    work page doi:10.1016/j.chb.2024.108388 2024

  3. [3]

    2010.A theory of constitutional rights

    Robert Alexy. 2010.A theory of constitutional rights. Oxford university press

    2010

  4. [4]

    George Beknazar-Yuzbashev, Rafael Jiménez Durán, Jesse McCrosky, and Mateusz Stalinski. 2022. Toxic content and user engagement on social media: Evidence from a field experiment.A vailable at SSRN 4307346(2022)

    2022

  5. [5]

    Nicole A Beres, Julian Frommel, Elizabeth Reid, Regan L Mandryk, and Madison Klarkowski. 2021. Don’t You Know That You’re Toxic: Normalization of Toxicity in Online Gaming. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA, 1–15. https://doi.org/10.1145/3411764.3445157

    work page doi:10.1145/3411764.3445157 2021

  6. [6]

    Don’t Feed the Troll

    Kelly Bergstrom. 2011. “Don’t Feed the Troll”: Shutting down Debate about Community Expectations on Reddit.Com. First Monday(July 2011). https://doi.org/10.5210/fm.v16i8.3498

    work page doi:10.5210/fm.v16i8.3498 2011

  7. [7]

    Shailee Bhatia, Vatsal Mittal, and Saransh Sabharwal. 2024. Building Trust and Transparency in AI: A Review of Explainable AI and Its Ethical Implications. In2024 International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN). 650–655. https://doi.org/10.1109/EmergIN63207.2024.10961081

    work page doi:10.1109/emergin63207.2024.10961081 2024

  8. [8]

    Stephanos Bibas. 2006. Transparency and participation in criminal procedure.NYUL rev.81 (2006), 911

    2006

  9. [9]

    Jeremy Blackburn and Haewoon Kwak. 2014. STFU NOOB! Predicting Crowdsourced Decisions on Toxic Behavior in Online Games. InProceedings of the 23rd International Conference on World Wide Web (WWW ’14). Association for Computing Machinery, New York, NY, USA, 877–888. https://doi.org/10.1145/2566486.2567987

    work page doi:10.1145/2566486.2567987 2014

  10. [10]

    2021.Thematic analysis: A Practical Guide

    Virginia Braun and Victoria Clarke. 2021.Thematic analysis: A Practical Guide. Sage Publications, Thousand Oaks, California, United States, 57–71

    2021

  11. [11]

    Kelly Caine. 2016. Local Standards for Sample Size at CHI. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16). Association for Computing Machinery, New York, NY, USA, 981–992. https: //doi.org/10.1145/2858036.2858498

    work page doi:10.1145/2858036.2858498 2016

  12. [12]

    Alessandro Canossa, Dmitry Salimov, Ahmad Azadvar, Casper Harteveld, and Georgios Yannakakis. 2021. For Honor, for Toxicity: Detecting Toxic Behavior through Gameplay.Proc. ACM Hum.-Comput. Interact.5, CHI PLAY (Oct. 2021), 253:1–253:29. https://doi.org/10.1145/3474680

    work page doi:10.1145/3474680 2021

  13. [13]

    Nancy Carter, Denise Bryant-Lukosius, Alba DiCenso, Jennifer Blythe, and Alan J. Neville. 2014. The Use of Triangula- tion in Qualitative Research.Oncology Nursing Forum41, 5 (Sept. 2014), 545–547. https://doi.org/10.1188/14.ONF.545- 547

    work page doi:10.1188/14.onf.545- 2014

  14. [14]

    Janine Natalya Clark. 2008. The Three Rs: Retributive Justice, Restorative Justice, and Reconciliation.Contemporary Justice Review11, 4 (Dec. 2008), 331–350. https://doi.org/10.1080/10282580802482603

    work page doi:10.1080/10282580802482603 2008

  15. [15]

    Russell Cropanzano, Marion Fortin, and Jessica F Kirk. 2015. How do we know when we are treated fairly? Justice rules and fairness judgments. InResearch in personnel and human resources management. Vol. 33. Emerald Group Publishing Limited, 279–350

    2015

  16. [16]

    John M Darley. 2009. Morality in the law: The psychological foundations of citizens’ desires to punish transgressions. Annual Review of Law and Social Science5, 1 (2009), 1–23

    2009

  17. [17]

    Saraswati Dawadi, Sagun Shrestha, and Ram A. Giri. 2021. Mixed-Methods Research: A Discussion on Its Types, Challenges, and Criticisms.Journal of Practical Studies in Education2, 2 (Feb. 2021), 25–36. https://doi.org/10.46809/ jpse.v2i2.20

    2021

  18. [18]

    Deci, Anja H

    Edward L. Deci, Anja H. Olafsen, and Richard M. Ryan. 2017. Self-Determination Theory in Work Organizations: The State of a Science.Annual Review of Organizational Psychology and Organizational Behavior4, Volume 4, 2017 (March 2017), 19–43. https://doi.org/10.1146/annurev-orgpsych-032516-113108

    work page doi:10.1146/annurev-orgpsych-032516-113108 2017

  19. [19]

    Eccles and Allan Wigfield

    Jacquelynne S. Eccles and Allan Wigfield. 2020. From Expectancy-Value Theory to Situated Expectancy-Value Theory: A Developmental, Social Cognitive, and Sociocultural Perspective on Motivation.Contemporary Educational Psychology 61 (April 2020), 101859. https://doi.org/10.1016/j.cedpsych.2020.101859

    work page doi:10.1016/j.cedpsych.2020.101859 2020

  20. [20]

    Dirk M. Elston. 2021. Participation Bias, Self-Selection Bias, and Response Bias.Journal of the American Academy of Dermatology0, 0 (June 2021). https://doi.org/10.1016/j.jaad.2021.06.025

    work page doi:10.1016/j.jaad.2021.06.025 2021

  21. [21]

    David S Evans. 2012. Governing bad behavior by users of multi-sided platforms.Berkeley Tech. LJ27 (2012), 1201

    2012

  22. [22]

    Barron, Christopher Hulleman, Betsy D

    Jessica Kay Flake, Kenneth E. Barron, Christopher Hulleman, Betsy D. McCoach, and Megan E. Welsh. 2015. Measuring Cost: The Forgotten Component of Expectancy-Value Theory.Contemporary Educational Psychology41 (April 2015), 232–244. https://doi.org/10.1016/j.cedpsych.2015.03.002 Proc. ACM Hum.-Comput. Interact., Vol. xx, No. x, Article CSCWxxx. Publication...

    work page doi:10.1016/j.cedpsych.2015.03.002 2015

  23. [23]

    Julian Frommel and Regan L Mandryk. 2024. Toxicity in Online Games: The Prevalence and Efficacy of Coping Strategies. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3613904.3642523

    work page doi:10.1145/3613904.3642523 2024

  24. [24]

    Lon Luvois Fuller. 1969. The morality of law. (1969)

    1969

  25. [25]

    2012.The concept of law

    Herbert Lionel Adolphus Hart and Leslie Green. 2012.The concept of law. oxford university press

    2012

  26. [26]

    J. J. P. Hilbers. 2015.An Evaluation of the Effectiveness of Smite’s Player Report System. Master’s thesis

    2015

  27. [27]

    2024.Toxicity in the Online Game League of Legends

    Kristine Ashley Holmes. 2024.Toxicity in the Online Game League of Legends. Master’s thesis. San Diego State University

    2024

  28. [28]

    Jonathan Jackson, Ben Bradford, Mike Hough, Andy Myhill, Paul Quinton, and Tom R Tyler. 2012. Why do people comply with the law? Legitimacy and the influence of legal institutions.British journal of criminology52, 6 (2012), 1051–1071

    2012

  29. [29]

    Sukwoo Jang, Minsun Hong, and Youn-kyung Lim. 2019. Ally-Opponent Understanding: Co-existence of Conflicting Values through Participatory Design.Design Works2, 2 (2019), 16–28

    2019

  30. [30]

    ROFL F*ck You

    Justin Juvrud. 2020.“ROFL F*ck You” : Understanding the Current State of Toxicity in Battlefield V

    2020

  31. [31]

    Gopinaath Kannabiran. 2021. I Am Sorry!interactions28, 5 (Aug. 2021), 14–15. https://doi.org/10.1145/3477089

    work page doi:10.1145/3477089 2021

  32. [32]

    Jawad Khan, Imran Saeed, Muhammad Zada, Amna Ali, Nicolás Contreras-Barraza, Guido Salazar-Sepúlveda, and Alejandro Vega-Muñoz. 2022. Examining whistleblowing intention: The influence of rationalization on wrongdoing and threat of retaliation.International journal of environmental research and public health19, 3 (2022), 1752

    2022

  33. [33]

    Bastian Kordyaka and Björn Kruse. 2021. Curing Toxicity – Developing Design Principles to Buffer Toxic Behaviour in Massive Multiplayer Online Games.Safer Communities20, 3 (Jan. 2021), 133–149. https://doi.org/10.1108/SC-10-2020- 0037

    work page doi:10.1108/sc-10-2020- 2021

  34. [34]

    Yubo Kou. 2020. Toxic Behaviors in Team-Based Competitive Gaming: The Case of League of Legends. InProceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY ’20). Association for Computing Machinery, New York, NY, USA, 81–92. https://doi.org/10.1145/3410404.3414243

    work page doi:10.1145/3410404.3414243 2020

  35. [35]

    Yubo Kou. 2021. Punishment and Its Discontents: An Analysis of Permanent Ban in an Online Game Community. Proc. ACM Hum.-Comput. Interact.5, CSCW2 (Oct. 2021), 334:1–334:21. https://doi.org/10.1145/3476075

    work page doi:10.1145/3476075 2021

  36. [36]

    Yubo Kou and Xinning Gui. 2021. Flag and Flaggability in Automated Moderation: The Case of Reporting Toxic Behavior in an Online Game Community. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3411764.3445279

    work page doi:10.1145/3411764.3445279 2021

  37. [37]

    Yubo Kou, Xinning Gui, Shaozeng Zhang, and Bonnie Nardi. 2017. Managing Disruptive Behavior through Non- Hierarchical Governance: Crowdsourcing in League of Legends and Weibo.Proc. ACM Hum.-Comput. Interact.1, CSCW (Dec. 2017), 62:1–62:17. https://doi.org/10.1145/3134697

    work page doi:10.1145/3134697 2017

  38. [38]

    Anastasia Kozyreva, Stefan M Herzog, Stephan Lewandowsky, Ralph Hertwig, Philipp Lorenz-Spreen, Mark Leiser, and Jason Reifler. 2023. Resolving content moderation dilemmas between free speech and harmful misinformation. Proceedings of the National Academy of Sciences120, 7 (2023), e2210666120

    2023

  39. [39]

    Samuli Laato, Bastian Kordyaka, and Juho Hamari. 2024. Traumatizing or Just Annoying? Unveiling the Spectrum of Gamer Toxicity in the StarCraft II Community. InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, New York, NY, USA, 1–18. https://doi.org/10.1145/3613904. 3642137

    work page doi:10.1145/3613904 2024

  40. [40]

    Vivian Lai, Samuel Carton, Rajat Bhatnagar, Q Vera Liao, Yunfeng Zhang, and Chenhao Tan. 2022. Human-ai collaboration via conditional delegation: A case study of content moderation. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 1–18

    2022

  41. [41]

    Matthew Lapolla. 2020. Tackling Toxicity: Identifying and Addressing Toxic Behavior in Online Video Games.Seton Hall University Dissertations and Theses (ETDs)(May 2020)

    2020

  42. [42]

    2021.’You cannot mute that someone walks under a turret and dies’:: Exploring League of Legends- players’ perception, definitions of toxicity and their effect on everyday life

    William Laumann. 2021.’You cannot mute that someone walks under a turret and dies’:: Exploring League of Legends- players’ perception, definitions of toxicity and their effect on everyday life. Master’s thesis. Uppsala University

    2021

  43. [43]

    Shyam Sundar

    Q.Vera Liao and S. Shyam Sundar. 2022. Designing for Responsible Trust in AI Systems: A Communication Perspective. InProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 1257–1268. https://doi.org/10.1145/3531146.3533182

    work page doi:10.1145/3531146.3533182 2022

  44. [44]

    2015-10-05Z

    Chwen-Yea Lin, Wei-Hsi Hung, Kwoting Fang, and Chien-Chung Tu. 2015-10-05Z. Understanding Players’ Achievement Values from MMORPGs: An Exploratory Study.Internet Research25, 5 (2015-10-05Z), 829–851. https://doi.org/10. 1108/IntR-12-2013-0268

    2015

  45. [45]

    2012.The bramble bush: On our law and its study

    Karl N Llewellyn. 2012.The bramble bush: On our law and its study. Quid Pro Books

    2012

  46. [46]

    Thodoris Lykouris and Wentao Weng. 2024. Learning to defer in content moderation: The human-ai interplay.arXiv preprint arXiv:2402.12237(2024)

    work page arXiv 2024

  47. [47]

    Renkai Ma, Yao Li, and Yubo Kou. 2023. Transparency, Fairness, and Coping: How Players Experience Moderation in Multiplayer Online Games. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI Proc. ACM Hum.-Comput. Interact., Vol. xx, No. x, Article CSCWxxx. Publication date: November 2026. Understanding Player Values and Exp...

    work page doi:10.1145/3544548.3581097 2023

  48. [48]

    Renkai Ma, Yue You, Xinning Gui, and Yubo Kou. 2023. How Do Users Experience Moderation?: A Systematic Literature Review.Proc. ACM Hum.-Comput. Interact.7, CSCW2 (Oct. 2023), 278:1–278:30. https://doi.org/10.1145/3610069

    work page doi:10.1145/3610069 2023

  49. [49]

    Kirsti Malterud, Volkert Dirk Siersma, and Ann Dorrit Guassora. 2016. Sample Size in Qualitative Interview Studies: Guided by Information Power.Qualitative Health Research26, 13 (Nov. 2016), 1753–1760. https://doi.org/10.1177/ 1049732315617444

    2016

  50. [50]

    Marcus Märtens, Siqi Shen, Alexandru Iosup, and Fernando Kuipers. 2015. Toxicity Detection in Multiplayer Online Games. In2015 International Workshop on Network and Systems Support for Games (NetGames). 1–6. https://doi.org/10. 1109/NetGames.2015.7382991

    work page arXiv 2015

  51. [51]

    Brian McInnis, Leah Ajmani, Lu Sun, Yiwen Hou, Ziwen Zeng, and Steven P Dow. 2021. Reporting the Community Beat: Practices for Moderating Online Discussion at a News Website.Proceedings of the ACM on Human-Computer Interaction5, CSCW2 (2021), 1–25

    2021

  52. [52]

    Lloyd Montgomery, Daniela Damian, Tyson Bulmer, and Shaikh Quader. 2018. Customer Support Ticket Escalation Prediction Using Feature Engineering.Requirements Engineering23, 3 (Sept. 2018), 333–355. https://doi.org/10.1007/ s00766-018-0292-3

    2018

  53. [53]

    Katherine Muenks, J. E. Miller, Brendan A. Schuetze, and Tiffany A. Whittaker. 2023. Is Cost Separate from or Part of Subjective Task Value? An Empirical Examination of Expectancy-Value versus Expectancy-Value-Cost Perspectives. Contemporary Educational Psychology72 (Jan. 2023), 102149. https://doi.org/10.1016/j.cedpsych.2023.102149

    work page doi:10.1016/j.cedpsych.2023.102149 2023

  54. [54]

    Marsh, L

    Benjamin Nagengast, Herbert W. Marsh, L. Francesca Scalas, Man K. Xu, Kit-Tai Hau, and Ulrich Trautwein. 2011. Who Took the “×” out of Expectancy-Value Theory?: A Psychological Mystery, a Substantive-Methodological Synergy, and a Cross-National Generalization.Psychological Science22, 8 (Aug. 2011), 1058–1066. https://doi.org/10.1177/ 0956797611415540

    2011

  55. [55]

    Louise Anker Nexø and Søren Kristiansen. 2023. Players Don’t Die, They Respawn: A Situational Analysis of Toxic Encounters Arising from Death Events in League of Legends.European Journal on Criminal Policy and Research29, 3 (Sept. 2023), 457–476. https://doi.org/10.1007/s10610-023-09552-y

    work page doi:10.1007/s10610-023-09552-y 2023

  56. [56]

    Ilse Oosterlaken. 2015. Applying Value Sensitive Design (VSD) to Wind Turbines and Wind Parks: An Exploration. Science and Engineering Ethics21, 2 (April 2015), 359–379. https://doi.org/10.1007/s11948-014-9536-x

    work page doi:10.1007/s11948-014-9536-x 2015

  57. [57]

    2017.Law and morality

    Leon Petrazycki and A Javier Trevino. 2017.Law and morality. Routledge

    2017

  58. [58]

    Susanne Poeller, Martin Johannes Dechant, Madison Klarkowski, and Regan L. Mandryk. 2023. Suspecting Sarcasm: How League of Legends Players Dismiss Positive Communication in Toxic Environments.Proc. ACM Hum.-Comput. Interact.7, CHI PLAY (Oct. 2023), 374:1–374:26. https://doi.org/10.1145/3611020

    work page doi:10.1145/3611020 2023

  59. [59]

    Robinson

    Susanne Poeller and Raquel B. Robinson. 2024. Mute, Block, Punish, Reward? A Call to Shift the Research Focus From Concealing Toxicity in Games to Promoting Genuine Positive Behavior. InCompanion Proceedings of the 2024 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY Companion ’24). Association for Computing Machinery, New York, NY, USA, ...

    work page doi:10.1145/3665463.3678863 2024

  60. [60]

    Antti Pohjanen. 2018. Report Please! : A Survey on Players’ Perceptions towards the Tools for Fighting Toxic Behavior in Competitive Online Multiplayer Video Games. (2018)

    2018

  61. [61]

    Richard A Posner. [n. d.]. ECONOMIC ANALYSIS OF LAW

  62. [62]

    Putwain, Laura J

    David W. Putwain, Laura J. Nicholson, Reinhard Pekrun, Sandra Becker, and Wendy Symes. 2019. Expectancy of Success, Attainment Value, Engagement, and Achievement: A Moderated Mediation Analysis.Learning and Instruction 60 (April 2019), 117–125. https://doi.org/10.1016/j.learninstruc.2018.11.005

    work page doi:10.1016/j.learninstruc.2018.11.005 2019

  63. [63]

    Arif Rachmatullah, Frieda Reichsman, Trudi Lord, Chad Dorsey, Bradford Mott, James Lester, and Eric Wiebe. 2021. Modeling Secondary Students’ Genetics Learning in a Game-Based Environment: Integrating the Expectancy-Value Theory of Achievement Motivation and Flow Theory.Journal of Science Education and Technology30, 4 (Aug. 2021), 511–528. https://doi.org...

    work page doi:10.1007/s10956-020-09896-8 2021

  64. [64]

    Randall and Maria F

    Donna M. Randall and Maria F. Fernandes. 1991. The Social Desirability Response Bias in Ethics Research.Journal of Business Ethics10, 11 (Nov. 1991), 805–817. https://doi.org/10.1007/BF00383696

    work page doi:10.1007/bf00383696 1991

  65. [65]

    Mandryk, Nicole A

    Elizabeth Reid, Regan L. Mandryk, Nicole A. Beres, Madison Klarkowski, and Julian Frommel. 2022. Feeling Good and In Control: In-game Tools to Support Targets of Toxicity.Proc. ACM Hum.-Comput. Interact.6, CHI PLAY (Oct. 2022), 235:1–235:27. https://doi.org/10.1145/3549498

    work page doi:10.1145/3549498 2022

  66. [66]

    2017.Content moderation

    Sarah T Roberts. 2017.Content moderation

    2017

  67. [67]

    Isabela Maria Rosal Santos, Felipe Silva, José Renato Laranjeira de Pereira, Mariane Andrade Moreira, Paulo Henrique Atta Sarmento, and Thiago Guimarães Moraes. 2020. Contribution for Santa Clara Principles-A contribution regarding transparency around the use of automated tools and decision-making. (2020)

    2020

  68. [68]

    Suman Roy, Durga Prasad Muni, John-John Yeung Tack Yan, Navin Budhiraja, and Fabien Ceiler. 2016. Clustering and Labeling IT Maintenance Tickets. InService-Oriented Computing, Quan Z. Sheng, Eleni Stroulia, Samir Tata, and Sami Bhiri (Eds.). Springer International Publishing, Cham, 829–845. https://doi.org/10.1007/978-3-319-46295-0_58 Proc. ACM Hum.-Compu...

    work page doi:10.1007/978-3-319-46295-0_58 2016

  69. [69]

    Richard M. Ryan, C. Scott Rigby, and Andrew Przybylski. 2006. The Motivational Pull of Video Games: A Self- Determination Theory Approach.Motivation and Emotion30, 4 (Dec. 2006), 344–360. https://doi.org/10.1007/s11031- 006-9051-8

    work page doi:10.1007/s11031- 2006

  70. [70]

    Santa Clara Principles Working Group. 2018. The Santa Clara Principles on Transparency and Accountability in Content Moderation. https://santaclaraprinciples.org Accessed: 2025-01-26

    2018

  71. [71]

    Schaefer, Jessie Y

    Kristin E. Schaefer, Jessie Y. C. Chen, James L. Szalma, and P. A. Hancock. 2016. A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Understanding Autonomy in Future Systems.Human Factors58, 3 (May 2016), 377–400. https://doi.org/10.1177/0018720816634228

    work page doi:10.1177/0018720816634228 2016

  72. [72]

    1991.Playing by the rules: A philosophical examination of rule-based decision-making in law and in life

    Frederick Schauer. 1991.Playing by the rules: A philosophical examination of rule-based decision-making in law and in life. Clarendon Press

    1991

  73. [73]

    Shields, Saul Shiffman, and Arthur Stone

    Alan L. Shields, Saul Shiffman, and Arthur Stone. 2010. Recall Bias: Understanding and Reducing Bias in PRO Data Collection. InePro. Routledge

    2010

  74. [74]

    Huiyoung Shin and Sunjeong Gyeong. 2025. When Motivation Isn’t Enough: Perceived Teacher Attitudes, Efficacy, and Efforts Moderate Student Responses to Bullying.BMC Psychology13, 1 (June 2025), 609. https://doi.org/10.1186/s40359- 025-02959-8

    work page doi:10.1186/s40359- 2025

  75. [75]

    Sparrow, Ren Galwey, Dahlia Jovic, Taylor Hardwick, and Mahli-Ann Butt

    Lucy A. Sparrow, Ren Galwey, Dahlia Jovic, Taylor Hardwick, and Mahli-Ann Butt. 2024. Towards Ethical AI Moderation in Multiplayer Games.Proc. ACM Hum.-Comput. Interact.8, CHI PLAY (Oct. 2024), 344:1–344:30. https: //doi.org/10.1145/3677109

    work page doi:10.1145/3677109 2024

  76. [76]

    Robin Steedman, Helen Kennedy, and Rhianne Jones. 2020. Complex Ecologies of Trust in Data Practices and Data- Driven Systems.Information, Communication & Society23, 6 (May 2020), 817–832. https://doi.org/10.1080/1369118X. 2020.1748090

    work page doi:10.1080/1369118x 2020

  77. [77]

    2018.Legal reasoning and political conflict

    Cass R Sunstein. 2018.Legal reasoning and political conflict. Oxford University Press

    2018

  78. [78]

    Selen Türkay, Jessica Formosa, Sonam Adinolf, Robert Cuthbert, and Roger Altizer. 2020. See No Evil, Hear No Evil, Speak No Evil: How Collegiate Players Define, Experience and Cope with Toxicity. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://d...

    work page doi:10.1145/3313831.3376191 2020

  79. [79]

    Tom R Tyler. 2001. Obeying the law in America: procedural justice and the sense of fairness.Issues of Democracy6, 1 (2001), 16–21

    2001

  80. [80]

    Mark S. Umbreit. 1989. Crime Victims Seeking Fairness, Not Revenge: Toward Restorative Justice.Federal Probation53 (1989), 52

    1989

Showing first 80 references.