Methodology
P(l)ay to Win is an academic research platform developed as part of a Master's thesis in Information Systems Management. It provides a structured environment for collecting, annotating, and classifying evidence of deceptive design patterns in video games.
Research Context
Deceptive design patterns — often referred to as "dark patterns" — are user interface choices crafted to trick, manipulate, or coerce users into actions they did not intend. While extensively studied in e-commerce and web services, their prevalence in video games has received comparatively less systematic attention.
This platform was built to address that gap. It serves as a crowdsourced evidence repository where researchers and participants can capture real-world instances of deceptive design in games, annotate the specific UI elements involved, and classify them according to a standardised taxonomy.
Research Objectives
- Catalogue observable instances of deceptive design in video game interfaces through participant-submitted evidence.
- Classify each instance according to an established taxonomy derived from the academic literature on dark patterns and deceptive design.
- Analyse the frequency, severity, and distribution of these patterns across titles, genres, and monetisation models.
- Contribute a publicly accessible dataset that supports future research into player protection and ethical game design.
Data Collection Process
Evidence Capture
Participants take screenshots or screen recordings of game interfaces that exhibit deceptive design patterns. Captures must show the full interface context and contain no personal data.
Spatial Annotation
Using the platform's bounding-box tool, participants draw regions around the specific UI elements that enact the deceptive pattern. Coordinates are stored as percentages to remain resolution-independent.
Taxonomy Classification
Each evidence entry is assigned one or more categories from the platform's taxonomy. The taxonomy is derived from established literature, covering patterns such as Nagging, Obstruction, Confirmshaming, Visual Hierarchy manipulation, Trick Questions, Sneaking, Forced Action, artificial Scarcity/Urgency, and misleading Social Proof Cues.
Descriptive Documentation
Participants provide a written description that explains what the pattern is, where it appears in the game, and how it manipulates or deceives the player. Descriptions should be objective, concise, and grounded in observable interface behaviour.
Taxonomy Foundation
The classification taxonomy used by P(l)ay to Win draws on foundational work in deceptive design research, including but not limited to:
- Brignull's original cataloguing of dark patterns in web interfaces.
- Gray et al.'s framework for assaultive, sneaking, and obstruction-based deceptive strategies.
- Mathur et al.'s large-scale measurement studies of dark patterns in e-commerce.
- Zagal et al.'s work on dark game design patterns and predatory monetisation in games.
The taxonomy has been adapted to the specific context of video game interfaces, where patterns often manifest through in-game shops, loot boxes, battle passes, and progression-gating mechanics.
Data Integrity
To maintain the quality and reliability of the dataset, the platform enforces several safeguards:
- All submissions require authentication, ensuring accountability and traceability.
- A 20-minute editing window allows authors to correct mistakes immediately after submission, after which the entry becomes immutable.
- Annotation coordinates are stored as resolution-independent percentages, ensuring they remain accurate regardless of display size.
Academic Context
This platform was developed as part of a Master's thesis in Information Systems Management. The research investigates the prevalence and nature of deceptive design patterns in contemporary video games, using P(l)ay to Win as the primary instrument for data collection and analysis.
Participation in the platform constitutes voluntary contribution to an academic research project. Collected data may be used in academic publications, always in aggregated or anonymised form.
Want to learn how to contribute?