Data-Driven Classifications of Video Game Vocabulary
March 13, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Nicolas Grelier, StΓ©phane Kaufmann
arXiv ID
2303.07179
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
As a novel and fast-changing field, the video game industry does not have a fixed and well-defined vocabulary. In particular, game genres are of interest: No two experts seem to agree on what they are and how they relate to each other. We use the user-generated tags of the video game digital distribution service Steam to better understand how players think about games. We investigate what they consider to be genres, what comes first to their minds when describing a game, and more generally what words do they use and how those words relate to each other. Our method is data-driven as we consider for each game on Steam how many players assigned each tag to it. We introduce a new metric, the priority of a Steam tag, that we find interesting in itself. This allows us to create taxonomies and meronomies of some of the Steam tags. In particular, in addition to providing a list of game genres, we distinguish what tags are essential or not for describing games according to players. Furthermore, we provide a small group of tags that summarise all information contained in the Steam tags.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted