"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, Shen, Robert Xiao
TLDR
Players report in games for altruistic and retributive reasons but often lack faith in system effectiveness due to developer reputation and transparency.
Key contributions
- Examined player reporting in games using expectancy-value theory.
- Found reporting is driven by both altruistic and retributive motivations.
- Players seek both short-term revenge and long-term community improvement.
- Player belief in reporting systems is mediated by developer reputation and transparency.
Why it matters
This paper highlights player motivations and frustrations with in-game reporting systems. Understanding these factors is crucial for designing more effective and trusted digital moderation tools. It offers insights for improving player satisfaction and fostering healthier online communities.
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.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.