Development and evolution of computational social science and its disciplinary framework and structure
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TP399;C03;C05

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    Abstract:

    Data-driven provides a great space for the rise and development of computational social science in social science research, enhances the depth and breadth of social science research, and effectively meets the complexity needs of scientific research. It is of great significance for the development of computational social science to explore the development and evolution of computational social science, and the definition of disciplinary framework and disciplinary structure. By collecting and arranging domestic and foreign literatures in the field of computational social science, this paper clarifies the concept of computational social science, and then sorts out the disciplinary evolution trend, research paradigm, research method and research application of computational social science. On the basis of the classification system of departments and disciplines, the discipline definition and discipline structure of computational social science are studied, and its relationship with the methods and technology disciplines is further analyzed. Then the disciplinary framework and disciplinary structure of computational social science are considered from a disciplinary perspective. Research conclusions: First, the number of foreign papers in computational social science is ahead of the number of domestic papers, and the computational social science research in China is still in its infancy. Second, the research focus of computational social science at home and abroad is different. Domestic research focuses on emerging topics such as artificial intelligence, complex systems, and communication theory, pays more attention to the analysis of data quality in the data-driven process, and emphasizes the use of methodologies such as modeling and simulation, social network analysis, and data mining. Foreign computational social science takes data science as the core, focuses on social media, social networks and complex systems, and emphasizes the use of methods such as social network analysis, agent-based modeling, machine learning, and natural language processing. Third, computational social science is a collection of second-level disciplines under traditional social science disciplines. It cannot be set as a first-level discipline. Digital humanities and computational social science must be strictly distinguished. Fourth, there are certain limitations in the field of computational social science research, and there are also certain deficiencies in height. Fifth, method and technology disciplines are important supports for computational social science, but as a discipline of social science methods and technology, it is not appropriate to set up secondary disciplines of computational social science, such as management science and engineering, and information resource management.

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俞立平,冉嘉睿,罗宇舟,买买提依明·祖农.计算社会科学发展演变及学科框架与学科结构[J].重庆大学学报社会科学版,2023,(2):124~139

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  • Online: May 08,2023
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