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The magic of computational social science: How it redefines the way we understand society?
In the current digital age, computational social science, as an interdisciplinary field, is influencing our understanding of social phenomena in unprecedented ways. By simulating, analyzing, and building computational models of social phenomena, this field is subverting traditional social science research and leading a new wave of social surveys and research.
Computational social science uses computing technology to analyze social behavior and interactions, emphasizes data-driven research methods, and inspires many unforeseen insights.
Computational social science is closely related to social science computing (SSC). The former focuses on studying social phenomena using computational methods, while the latter is dedicated to creating computational models to explain these phenomena. This distinction helps us gain a deeper understanding of how this field builds a bridge between theory and application.
By analyzing the digital footprint of data, computational social science has provided a huge boost to empirical research and has taken the construction of scientific theories to a new level.
In computational social science research, the diversity of data sources is absolutely its charm. From data analysis of social media to exploration of traditional media content, researchers are constantly mining the various available databases. For example, the Seshat Global Historical Database provides comprehensive data on the evolution of human social organization, while D-PLACE focuses on data on the formation of human societies; these databases allow researchers to conduct more detailed analyses of social behavior.
Computational social science depends on the availability of large databases, which are often built and maintained by multidisciplinary collaborative projects.
Computational social sciences have diverse research methods, and scholars are exploring new capabilities and methods in their respective professional fields. Nelimarkka proposed five typical methods of computational social science, including data-driven method, method-driven method, model-driven method, digital society-focused method and social theory perspective. These methods not only focus on the application of technology, but also emphasize the extension and innovation of theory.
Data-driven methods allow researchers to gain new knowledge from previously untapped sources of social data, thereby challenging traditional social science theories.
As computational social science continues to develop, the channels for scholarly publishing are becoming increasingly diverse. Related research articles have been published in multiple journals such as New Media and Society and Social Science Computing Review. At the same time, some conferences focusing on computational social science have also attracted much attention, such as the International Conference on Computational Social Science (IC2S2). These channels provide a platform for scholars and researchers to share their latest research results, promoting the exchange and dissemination of knowledge.
The rapid development of computational social science not only challenges many traditional research methods, but also provides fertile ground for the study of emerging trends.
The influence of computational social science is growing day by day. This field has shown great potential and flexibility both in academia and in social practice. When we look for patterns in social behavior in data, are we also reshaping our view of society?
