Using Keyword Networks to Define Indicators of Meeting Dynamics

A Lightweight Approach for Analyzing Discussion Dynamics and Topic Structures

Abstract

This study presents a lightweight method for evaluating creative meeting dynamics using keyword co-occurrence networks. Traditional approaches like idea fluency or expert evaluations often fail to capture the nuances of discussions and are challenging to implement consistently, while computational methods like Large Language Models (LLMs) are resource-intensive. Using data from 53 real-world business meetings (average duration: 1.5 hours), we analyzed accumulated and non-accumulated metrics from keyword networks to define key features through Principal Component Analysis (PCA).Three main indicators—Discussion Expansion, Local Intensity, and Topic Variety—were developed to evaluate meeting quality. These indicators effectively differentiate high-performing meetings, capturing both cumulative trends and moment-to-moment dynamics. This framework offers a scalable and cost-efficient alternative for assessing meeting creativity and productivity, with potential applications in improving team performance and collaboration. By leveraging lightweight methods, the study bridges the gap between practical usability and analytical rigor in meeting evaluation.