Thematic Analysis And Interdisciplinarity Of Computational Social Science

With the development of information technology and big data,researchers can observe and explore the characteristics of individual behaviors,the laws of social operation and the interaction between them by information technology,which changes traditional social sciences research methods.To response the construction of new generation of Humanity and Social Sciences,computational social science provides more space and dimensions.This thesis summarizes the theme change and characteristics of computational social science from bibliometric perspectives and measures the interdisciplinarity of computational social sciences.It is expected to provide a new research perspective for computational social science,and make certain knowledge contributions to explore the potential research topics of computational social science.Firstly,based on the core collection of Web of Science data,this thesis analyzes the output,collaboration,and citation impact of computational social science,and finds that during 2006-2020,the number of publications in this field has shown rapid growth,of which the United States take the lead.The United Kingdom and China rank second and third,but with a large gap between the United States in the total amount.In terms of international collaboration,the United States is at the core of cooperation and has many partners.Although China is also near in the core of cooperation,partners are relatively limited.not as extensive as the United States,the United Kingdom,and other developed countries.From the perspective of institutional collaboration,there is a relatively obvious trend of geographical clustering.The publication quality of China is highly skewed: the proportion of high-quality papers is high whereas that of the average level is below the global average.Secondly,in order to have a more intuitive understanding of the research topics of computational social science,this thesis presents the distribution of the topic hotspots by drawing a knowledge map.Computational social science absorbs knowledge from a wide range of disciplines,sucha as psychology,physics,management,and public safety,with knowledge impact on environmental science,public health,and other directions.Although with a wide range of research topics,the studies in computational social sciencemainly surrounded in disciplines like public management,sociology,pedagogy,and communication,with topics like information ethics,social decision-making,and public opinion.A very distinctive theme has not formed yet.However it is closely integrated with the hot topics at the time,involving technical methods such as big data,artificial intelligence,and deep learning,and some traditional social science research methods,such as complex network,ABM,and social network analysis also run through it all the time.Finally,in view of the significant interdisciplinary characteristics of computational social science,the DIV indicators are used to quantitatively measure the interdisciplinarity of computational social science so as to quantitatively analyze the degree of knowledge integration.The DIV indicator covers three dimensions,namely,balance,diversity,and variety.Studies have shown that the balance of computational social science is at a high level,diversity is at a medium level,and variety is at a low level.Out of expectation,the interdisciplinarity of computational social science is only at a low level,but the value tends to increase by time.In view of the fact that social science and computer and information science are the main subjects involved in computational social science,this thesis conducts an in-depth analysis of the interdisciplinarity of the research topics of these two disciplines and finds that the interdisciplinarity of papers belonging to social science is higher than papers belonging to the computer and information sciences,and the interdisciplinarity of the papers under the two disciplines shows an opposite trend.