Mapping the socio-technical evolution of information overload: a bibliometric and topic modeling analysis

Abstract

This study aims to systematically map the evolution, influence structure and thematic organization of information overload research and to uncover latent research directions not directly observable from bibliometric metadata, with particular attention to the socio-technical dynamics of the digital workplace. A corpus of 1,081 English-language research articles, review articles and conference papers indexed in the Web of Science Core Collection and Scopus and published between 2000 and March 2026 was analyzed. The study combines descriptive bibliometric indicators, keyword co-occurrence network analysis and Latent Dirichlet Allocation (LDA) topic modeling to examine the structural and semantic development of the field. Research on information overload has expanded rapidly since 2018, reaching a peak in 2025. The literature exhibits a pronounced socio-technical structure linking algorithmic filtering, information systems, knowledge management, managerial decision-making and employee well-being. Topic modeling identifies eight major themes, with recommender systems and social media in digital work environments emerging as the most prominent and fastest-growing streams. This article advances information overload research by integrating bibliometric science mapping and machine learning within a unified analytical framework. It shows how the field has evolved from cognitive and communication-centered perspectives toward a broader socio-technical understanding of digital work and offers a structured agenda for cumulative theory development.