Transactions on Data Analysis in Social Science (TDAS) aims to serve as a leading international platform for the dissemination of high-quality, peer-reviewed research at the intersection of data analysis and social science. The journal seeks to advance theoretical understanding, methodological development, and practical applications of data-driven approaches for analyzing complex social, economic, behavioral, and institutional phenomena.
The scope of TDAS encompasses original research articles, review papers, and methodological studies that employ quantitative, qualitative, and mixed-methods approaches, as well as classical statistical techniques and advanced data analytics methods. The journal particularly encourages interdisciplinary and innovative research that integrates data science, machine learning, statistical modeling, and computational social science with core social science disciplines.
TDAS welcomes contributions in, but not limited to, the following areas:
Data-driven analysis in Communication and Media Studies
Quantitative and computational methods in Economics and Management
Behavioral data analysis in Psychology and Education
Legal analytics and empirical studies in Law
Political data analysis and computational political science
Social data mining and sociological analysis
Administrative and public policy analytics
Big data, machine learning, and artificial intelligence applications in social sciences
Text mining, sentiment analysis, network analysis, and social media analytics
By promoting methodological rigor, transparency, and interdisciplinary collaboration, TDAS aims to contribute to evidence-based research and policy-making, and to foster the development of innovative analytical frameworks that enhance the understanding of social systems in an increasingly data-rich world.