Conceptual Framework for Group Dynamics Modeling from Group Chat Interactions
Group Recommender Systems aim to support groups in making collective decisions, and research has consistently shown that the more we understand about group members and their interactions, the better support such systems can provide. In this work, we propose a conceptual framework for modeling group dynamics from group chat interactions, with a particular focus on decision-making scenarios. The framework is designed to support the development of intelligent agents that provide advanced forms of decision support to groups. It consists of modular, loosely coupled components that process and analyze textual and multimedia content, which is shared in group interactions, to extract user preferences, emotional states, interpersonal relationships, and behavioral patterns. By incorporating sentiment analysis, summarization, dialogue state tracking, and conflict resolution profiling, the framework captures both individual and collective aspects of group behavior. Unlike existing approaches, our model is intended to operate dynamically and adaptively during live group interactions, offering a novel foundation for group recommender and decision support systems.