A Human-Centred Profiling Approach to Characterise User Roles in Information Propagation on Online Social Networks

Online Social Networks (OSNs) have become essential behavioural data sources psychology, sociology, marketing and computer science. In the study of Information Propagation (IP) on OSNs, previous research has often focused on message content or network structures, neglecting users’ cognitive and emotional processes. This article proposes a human-centred profiling approach that holistically integrates user profile features, network position, activity patterns, and emotional dynamics to better model user roles in IP. Capturing internal decision-making and emotional resilience is critical to understanding information diffusion beyond traditional structural or semantic analyses. To our knowledge, no prior work has integrated these multidimensional user features to infer interpretable roles within IP processes. To operationalise this perspective, we constructed a feature-rich dataset based on Twitter (X) interactions, combining profile, network, activity, emotional and sentiment features. Using unsupervised learning techniques, specifically K-Means clustering and Principal Component Analysis (PCA), we identified four emergent user profiles: High-Credibility Informants, Emotionally-Driven Amplifiers, Mobilisation-Oriented Catalysts and Emotionally Exposed Participants. Our findings reveal that users are not passive conduits but active agents whose discernment, emotional regulation, and digital literacy shape propagation dynamics. This human-centred approach improves the interpretability of user profiles and offers new insights for ethical applications in recommendation systems, misinformation control, and digital literacy development, thus contributing to more resilient and ethically grounded information ecosystems.