In Supply Chain Risk Management (SCRM), the use of text mining is gaining increasing attention as the application of modern algorithms enable rapid, real-time interpretation of huge volume of unstructured data. Online data has the potential to provide critical insights for proactive risk identification and mitigation in the complex and interconnected global supply chain networks, especially in periods of man-made disasters or crises such as the Ukrainian-Russian conflict. This paper delivers an overview of how text mining of online data is being used in SCRM. More specifically, by using bibliometric methodologies, this study identifies primary areas of focus and collaborative networks in SCRM’s research field. The most common analysis methods such as citation analysis, co-authorship patterns, keyword co-occurrence, and publication trends take place as a comprehensive corpus of peer-reviewed articles from Scopus database is collected and analyzed. By employing bibliometric tools and network analysis measures the study explores how the use of text mining techniques is evolving for the past ten years, and where it is heading. This is achieved by visualizing the networks with suitable programmes, by using indices like the modularity index and with cluster analysis which are applied to the networks created from the bibliographic data. This study aims to uncover trends, disciplines, and how individual studies are related to one another. The results suggest thematic clusters, influential studies and authors, and research methods that are significant to the field and they map trends and future directions, providing valuable insights for both academic researchers and industry practitioners.