A real-time speech interaction analytics framework for group activities using SNA and LLM techniques

Abstract

In the current digital era, analyzing the dynamics of interaction in groups presents challenges in fields such as education, the business sector, and healthcare. The lack of integrated tools that monitor and evaluate discursive and social interactions in real-time makes it difficult to understand the flow of collaboration, the formation of effective teams, or the monitoring of social cognitive processes. In this article, we present a framework designed to analyze speech interactions in group activities by combining Social Network Analysis (SNA) and Large Language Models (LLM). Naira enables the real-time capture, processing, and analysis of speech interaction data, providing tools to evaluate discursive effectiveness and collaborative dynamics. The framework’s components are detailed in its different stages, and application cases are explored in educational, business, and healthcare contexts. A proof of concept in an educational environment proves the versatility and potential of the proposal to improve the understanding and optimization of group processes. Integrating SNA and LLM offers a comprehensive perspective combining validated and interpretable techniques to analyze attribute and relational variables with advanced and current artificial intelligence techniques. The framework’s main innovation lies in its ability to fuse the quantitative structural analysis of SNA with the semantic and qualitative content analysis of LLMs, offering a novel perspective that overcomes the limitations of each technique in isolation.