Big Data-Driven Transformation of Social Science Research - Tianhuan PENG (Journal of Systems Engineering and Information Technology (JOSEIT), 2025)
This study examines the transformative potential of big data integration in social science research and proposes systematic approaches for implementing data-driven methodologies through institutional innovation. The methodology encompasses systematic analysis of traditional research limitations, mechanistic investigation of big data integration effects on seven critical research phases (topic identification, literature synthesis, theoretical framework development, data acquisition, analytical processing and visualization, knowledge dissemination, and outcome evaluation), and institutional design analysis for philosophy and social science laboratory development. The analysis reveals that big data fundamentally transforms social science research through multiple mechanisms. The proposed laboratory framework addresses critical implementation challenges through seven strategic dimensions: institutional awareness enhancement, differentiated development pathways, computational infrastructure strengthening, specialized tool and platform development, interdisciplinary talent cultivation, comprehensive data resource construction, and supportive policy framework establishment.