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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.10263 |
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| _version_ | 1866912642751266816 |
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| author | Kanwal, Moona Siddiqui, Muhammad Sami Ali, Syed Anael |
| author_facet | Kanwal, Moona Siddiqui, Muhammad Sami Ali, Syed Anael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning Kanwal, Moona Siddiqui, Muhammad Sami Ali, Syed Anael Human-Computer Interaction Artificial Intelligence Computers and Society Machine Learning 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. |
| title | Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2510.10263 |