Joint-Workshop CEFC-CUHK (SZ)

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China Perspectives Special Issue: “China’s populations through the lens of Computational Social Science”

In the past decade, the digitalization of archives and government announcements and policies, and the possibility to harvest social media posts, have provided social scientists with new data to explore societies.

For instance, social scientists are now able to predict with 90% accuracy whether an American Facebook user leans Republican or Democrat by using advanced computational techniques such as machine learning and deep learning algorithms[1]. In African countries, where accurate and exhaustive census data can be hardly produced, social scientists can analyze mobile phone data to estimate poverty and trace its evolution[2]. Regarding population fertility and aging, which is a crucial matter for China’s future, Agent-Based Modeling offers a cost-efficient possibility to estimate the evolution of fertility, sex birth ratio, and the future age pyramid of a given country[3]. To finish on this non-exhaustive list, social movement coordination and the emotions they provoke, are often investigated using advanced methods in Social Network Analysis, topic modeling, and sentiment analysis, sometimes with the help of Chat-GPT-like large language models[4].

Chinese social scientists are also part of this worldwide trend and have developed ambitious projects to understand Chinese society and/or to inform local and national policy-makers. While we can’t report exhaustively the research breakthrough accomplished by Chinese computational social scientists, there is a necessity to make their research more visible and understandable to the community of researchers unfamiliar with their work.

This seminar therefore aims to present ongoing research, with a view to publishing a special issue of the journal China Perspectives, edited and published by the CEFC.

The programme of the workshop :

[1] Che-Chia Chang, Shu-I Chiu, and Kuo-Wei Hsu, “Predicting Political Affiliation of Posts on Facebook,” in Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication (IMCOM ’17: The 11th International Conference on Ubiquitous Information Management and Communication, Beppu Japan: ACM, 2017), 1–8, https://doi.org/10.1145/3022227.3022283.

[2] Joshua Blumenstock, Gabriel Cadamuro, and Robert On, “Predicting Poverty and Wealth from Mobile Phone Metadata,” Science 350, no. 6264 (November 27, 2015): 1073–76, https://doi.org/10.1126/science.aac4420.

[3] André Grow and Jan Van Bavel, eds., Agent-Based Modelling in Population Studies: Concepts, Methods, and Applications, vol. 41, The Springer Series on Demographic Methods and Population Analysis (Cham: Springer International Publishing, 2017), https://doi.org/10.1007/978-3-319-32283-4.

[4] Fatih Demir, Mehmet F Bastug, and Aziz Douai, “Keeping It Peaceful: Twitter and the Gezi Park Movement,” Communication and the Public5, no. 3–4 (September 2020): 149–63, https://doi.org/10.1177/2057047320959852; Sandra González-Bailón, Javier Borge-Holthoefer, and Yamir Moreno, “Broadcasters and Hidden Influentials in Online Protest Diffusion,” American Behavioral Scientist 57, no. 7 (2013): 943–65.