Computational Social Science: Application in China Studies

This edited collection provides an overview of the recent developments in computational social science related to China studies and presents interdisciplinary empirical work from diverse scholars on culture, public opinion, and education using advanced computational methods and big data. The topics covered in this book include the surge of anti-China sentiment amid the COVID-19 pandemic, the nuances of E-governance, public opinion, authoritarian reactions, artistic innovation, and educational inequality. The chapters in this book provide important insights into how computational social science can be applied generally, but also underscore the importance of combining conventional sociological research with contemporary computational methods in the context of China studies. This cutting-edge volume will be valuable resource for researchers, scholars and practitioners of Sociology, China Studies and for those interested in computational approaches to the social sciences. The chapters in this book were originally published in Chinese Sociological Review.

Table of Contents:
Introduction 1. Between reality and perception: the mediating effects of mass media on public opinion toward China 2. “Just a virus” or politicized virus? Global media reporting of China on COVID-19 3. Authoritarian responsiveness and political attitudes during COVID-19: evidence from Weibo and a survey experiment 4. Does the crying baby always get the milk? An analysis of government responses for online requests 5. Bowing to five pecks of rice: how online monetization programs shape artistic novelty 6. Leveraging machine learning methods to estimate heterogeneous effects: father absence in China as an example

About the Author :
Xiaogang Wu is Yufeng Global Professor of Social Science, Professor of Sociology at New York University and NYU Shanghai, and the Founding Director of Center for Applied Social and Economic Research (CASER) at NYU Shanghai, China. Wu is a leading scholar in research on Chinese inequality and social stratification. He has published over 70 articles in peer-reviewed journals, He has been serving as the Chief Editor of the Chinese Sociological Review since 2011, Associate Editor (Social Stratification) of Sociology Compass (2023-2025), and Global Scholar at Princeton University (2020-2024). Yongjun Zhang is Assistant Professor in the Department of Sociology and the Institute for Advanced Computational Science at Stony Brook University, USA. He uses big data with statistical, network, and computational methods to study political, social, and organizational behavior in the United States and across the globe. His current main projects include spatial segregation and mobility, political polarization, and global visual representation of social movements. His work has appeared in both interdisciplinary and sociological journals such as American Journal of Sociology, Demography, Scientific Reports, and Humanities and Social Sciences Communications. Tianji Cai is Professor of sociology in University of Macau, China. He received his PhD degree at University of North Carolina at Chapel Hill in 2010. His research interests concentrate on new forms of data and new methods of analysis. Reflecting on his broad intellectual pursuits, his research topics are diverse, ranging from methodological, such as quantitative methods and data mining, to substantive ones, such as gene-environmental interplay and adolescent health behaviours. He has published widely in leading international journals including American Sociological Review, American Journal of Sociology, Demography, Sociological Methodology, Sociological Methods & Research, Chinese Sociological Review.