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Paths Study on Knowledge Convergence and Development in Computational Social Science: Data Metric Analysis Based on Web of Science

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  • Yuxi Liu
  • Xin Feng
  • Yue Zhang
  • Ying Kong
  • Rongyao Yang

Abstract

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long‐term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis from a bibliometric perspective, aiming to provide a reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality.

Suggested Citation

  • Yuxi Liu & Xin Feng & Yue Zhang & Ying Kong & Rongyao Yang, 2022. "Paths Study on Knowledge Convergence and Development in Computational Social Science: Data Metric Analysis Based on Web of Science," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:3200371
    DOI: 10.1155/2022/3200371
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    References listed on IDEAS

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    1. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large‐scale research assessments," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
    2. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
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