Proposal of Diversity Evaluation Methods for News Article Viewing Using BERTopic and JS Divergence

Abstract

With the advancement of information recommendation systems, users' news consumption has increasingly become biased toward specific perspectives, exacerbating social issues such as polarization and division. This has raised concerns about the deterioration of “informational health”, where users originally maintained a balanced intake of diverse information. To address this, it is essential to understand user information consumption tendencies from the perspective of diversity. In this study, we analyze a large-scale dataset comprising news articles and user browsing logs. We employ BERTopic to convert news articles into topic distributions and evaluate users' news consumption diversity by applying Jensen-Shannon (JS) divergence to the traditionally used GS-score. This approach enables a refined assessment of users' browsing tendencies. Our results demonstrate that the proposed method outperforms the conventional GS-score in evaluating diversity. Furthermore, through topic-level analysis, we provide a more granular and detailed understanding of the relationship between news diversity and user browsing tendencies.