Titlebook: Sociological Foundations of Computational Social Science; Yoshimichi Sato,Hiroki Takikawa Book 2024 The Editor(s) (if applicable) and The

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Sociological Foundations of Computational Social Science978-981-99-9432-8Series ISSN 2197-8832 Series E-ISSN 2197-8840

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Sociological Foundations of Computational Social Science,modeling enhance sociological research. I highlight the strengths of CSS, like its ability to handle large-scale, continuous, and nonreactive data, which traditional methods cannot. This capability is exemplified in Chang et al. (2021), where mobile data analysis provided new insights into social in

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Methodological Contributions of Computational Social Science to Sociology,iology was survey data. Collecting survey data entailed significant financial and human costs; therefore, the data provided by surveys were scarce. Such data are clean, structured, and collected by probability sampling. In the digital age, however, people’s behavior is observed daily and recorded co

谎言

Computational Social Science: A Complex Contagion,formance computing: computer simulation, deep learning, natural language processing, and data wrangling with digital traces of millions of online interactions. The importance of social network analysis across these diverse methodologies opened an opportunity for Sociology to become the disciplinary

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吹牛者

Polarization of Opinion,oncept of opinion polarization remains intricate, encompassing various mechanisms of formation and context-dependent factors. Consequently, the mechanism of opinion polarization has yet to be conclusively demonstrated. Recently, the proliferation of digital data, combined with an array of computatio

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Coda,porating sociological concepts of meaning and interpretation into CSS, which has traditionally focused on behavioral data analysis. Previous chapters demonstrated methods for embedding these concepts in agent-based modeling and digital data analysis. We also discuss the potential of CSS, particularl