计算社会科学发展演变及学科框架与学科结构

数据驱动为计算社会科学在社会科学研究的兴起与发展提供了极大施展空间,提升了社会科学研究的深度与广度,有效契合科学研究的复杂性需求。探究计算社会科学发展演变、学科框架与学科结构的界定对于计算社会科学的发展具有重要意义。本文通过收集、整理计算社会科学领域国内外文献,厘清计算社会科学的概念,进而梳理计算社会科学的学科演进趋势、研究范式、研究方法及研究应用;并从数字人文的学科结构入手,分析教育部学科门类分类体系,在此基础上对计算社会科学的学科界定、学科结构进行研究,进一步分析其与方法、技术类学科的关系,进而从学科角度思考计算社会科学的学科框架与学科结构。研究结论：第一,计算社会科学国外论文数量领先于国内论文数量,我国计算社会科学研究尚处于起步阶段。第二,计算社会科学国内外研究侧重点不同。国内计算社会科学研究聚焦于人工智能、复杂系统、传播理论等新兴主题,更加重视数据驱动过程中数据质量的分析,强调通过建模仿真、社会网络分析、数据挖掘等方法论的使用。而国外计算社会科学以数据科学为核心,聚焦于社交媒体、社会网络与复杂系统,强调通过社会网络分析、基于Agent建模、机器学习、自然语言处理等方法使用。第三...

Development and evolution of computational social science and its disciplinary framework and structure

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.