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.
Abstract (translated)
对游戏玩家进行剖析，为适应性游戏设计、行为理解以及数字福祉提供了关键见解。本研究提出了一种结合心理测量、行为分析和机器学习的数据驱动综合框架，旨在揭示潜在的玩家人格特征。通过针对250名参与者（其中包括113位活跃游戏玩家）开展的一项结构化调查，收集了多维度的行为、动机及社交数据。分析流程整合了特征工程、关联网络、知识图谱分析以及无监督聚类技术来提取有意义的模式。 相关性统计使用了Cramér's V、Tschuprow's T、Theil's U和Spearman的相关度量，同时利用网络中心性指导特征选择。通过主成分分析（PCA）、奇异值分解（SVD）及t-SNE等降维技术与K-Means、凝聚层次聚类、谱聚类及DBSCAN等聚类算法相结合，并采用Silhouette、Calinski-Harabasz和Davies-Bouldin指数进行评估。使用主成分分析结合K-Means（k=4）的模型实现了最高的聚类质量，Silhouette系数为0.4，识别出了四种玩家原型：沉浸式社交故事寻找者、纪律严明的优化者、战略系统导航者和竞争团队建设者。 这项研究提供了一个可重复使用的流程，该流程将基于相关性的网络洞察与无监督学习相结合。行为相关性网络与聚类技术的结合不仅提升了分类准确性，而且还为连接游戏动机与心理及健康成果提供了整体视角。
URL
https://arxiv.org/abs/2510.10263
