INFORMATION-SEEKING BEHAVIOR AS A FEATURE FOR MACHINE LEARNING–DRIVEN USER CLASSIFICATION

Abstract

The rapid expansion of digital platforms has led to diverse patterns of online user behavior, particularly in how individuals search for, access, and interact with information. Understanding these patterns is essential for applications such as personalized recommendations, targeted marketing, cybersecurity, and digital forensics. This paper presents a machine learning approach to the classification of online users by exploiting their information-seeking behavior. By analyzing search queries, browsing histories, and navigation trails, the proposed framework identifies latent behavioral features that serve as strong predictors of user categories. Advanced algorithms such as decision trees, support vector machines, and neural networks are employed to capture both linear and nonlinear patterns in user behavior. Feature engineering techniques, including term frequency–inverse document frequency (TF-IDF) representations and sequential activity modeling, are used to enhance classification accuracy. Experimental results demonstrate that integrating behavioral features with machine learning significantly improves classification performance compared to conventional demographic-based approaches. This research highlights the potential of behavioral data as a valuable resource for accurate and adaptive online user classification, with implications for recommender systems, online security, and human–computer interaction.