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Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning

Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali

TL;DR

An integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas is proposed and contributes a reproducible pipeline that links correlation-driven network insights with unsupervised learning.

Abstract

Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association-network, knowledge-graph analysis, and unsupervised clustering to extract meaningful patterns. Correlation statistics uses Cramers V, Tschuprows T, Theils U, and Spearmans quantified feature associations, and network centrality guided feature selection. Dimensionality-reduction techniques such as PCA, SVD, t-SNE are coupled with clustering algorithms like K-Means, Agglomerative, Spectral, DBSCAN, evaluated using Silhouette, Calinski Harabasz, and Davies Bouldin indices. The PCA with K-Means with k = 4 model achieved optimal cluster quality with Silhouette = 0.4, identifying four archetypes as Immersive Social Story-Seekers, Disciplined Optimizers, Strategic Systems Navigators, and Competitive Team-Builders. This research contributes a reproducible pipeline that links correlation-driven network insights with unsupervised learning. The integration of behavioral correlation networks with clustering not only enhances classification accuracy but also offers a holistic lens to connect gameplay motivations with psychological and wellness outcomes.

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning

TL;DR

An integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas is proposed and contributes a reproducible pipeline that links correlation-driven network insights with unsupervised learning.

Abstract

Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association-network, knowledge-graph analysis, and unsupervised clustering to extract meaningful patterns. Correlation statistics uses Cramers V, Tschuprows T, Theils U, and Spearmans quantified feature associations, and network centrality guided feature selection. Dimensionality-reduction techniques such as PCA, SVD, t-SNE are coupled with clustering algorithms like K-Means, Agglomerative, Spectral, DBSCAN, evaluated using Silhouette, Calinski Harabasz, and Davies Bouldin indices. The PCA with K-Means with k = 4 model achieved optimal cluster quality with Silhouette = 0.4, identifying four archetypes as Immersive Social Story-Seekers, Disciplined Optimizers, Strategic Systems Navigators, and Competitive Team-Builders. This research contributes a reproducible pipeline that links correlation-driven network insights with unsupervised learning. The integration of behavioral correlation networks with clustering not only enhances classification accuracy but also offers a holistic lens to connect gameplay motivations with psychological and wellness outcomes.

Paper Structure

This paper contains 33 sections, 11 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Education & Learning
  4. Business & Marketing
  5. Gamer Motivations, Typologies, and Health
  6. Gamer Motivations and Typologies
  7. Personality and Neurobiological Perspectives
  8. Health and Well-being Implications
  9. Integrative Clustering Approaches
  10. Computer Science & AI
  11. Game Design
  12. Methodology
  13. Survey Design and Data Collection
  14. Data Pre-processing
  15. Statistical Analysis
  16. ...and 18 more sections

Figures (10)

  • Figure 1: Landscape of research directions in gamer profiling.
  • Figure 2: Flowchart of research
  • Figure 3: (a) Positive skewness in hard_disconnecting feature before merging; (b) after merging categories.
  • Figure 4: Correlation heatmaps of significant associations: (Up) Cramér's V; (Down) Spearman's $\rho$.
  • Figure 5: Knowledge graph (edges scaled by association weight); repertoire breadth and affect act as major hubs/bridges.
  • ...and 5 more figures