사회적 네트워크의 최근 연구동향 분석: semantic network analysis를 중심으로 = Analysis of Recent Research Trends in Social Networks: Focusing on Semantic Network Analysis

다국어 초록 (Multilingual Abstract)

This study conducted a quantitative analysis of research trends over the past decade by applying text mining and semantic network analysis to academic papers related to “social networks” published in Korea Citation Index (KCI) journals between 2015 and 2024. The analysis was divided into the general field of social sciences and the field of business-related studies, focusing on keyword frequency, centrality, and CONCOR analysis.
The results showed that in the overall social sciences domain, key research topics included “social network analysis,” “social capital,” “social economy,” and “social performance.” In contrast, in the business-related field, prominent keywords were “absorptive capacity,” “innovative behavior,” “knowledge sharing,” and “density.” The CONCOR analysis revealed that research in the social sciences was clustered around social issues related to the elderly and adolescents, as well as topics such as social enterprises and performance. In the business domain, research was primarily clustered around individual decision-making factors (e.g., entrepreneurial intention, retirement preparedness) and corporate performance outcomes.
The low betweenness centrality observed in the network analysis suggests that social network research has largely focused on specific themes, indicating limited topic diversity within the field. This highlights the need for interdisciplinary and integrative research approaches that can address a broader range of themes within social network studies. Furthermore, considering that social networks are increasingly being applied across diverse domains―including organizational performance, social capital, and psychological variables―future research should explore comparative studies across disciplines, and the application of multilayered network analysis techniques.
Ultimately, this study contributes to the field by providing a big data-based quantitative framework for understanding the macro-level trends in social network research. It offers a foundation for future theoretical expansion and empirical design, while also demonstrating the applicability of quantitative methodologies such as text mining and semantic network analysis within the context of social network research.