Document Type
Conference Proceeding
Publication Date
1-1-2021
Journal / Book Title
Proceedings of the Annual Hawaii International Conference on System Sciences
Abstract
This paper investigates the relationship between demographics and the frequency of censored posts (weibos) on Sina Weibo. Our results indicate that demographics such as location, gender and paid for features do not provide a good degree of predictive power but help explain how censorship is applied on social media. Using a dataset of 226 million weibos collected in 2012, we apply a binomial regression model to evaluate the predictive quality of user demographics to identify candidates that may be targeted for censorship. Our results suggest male users who are verified (pay for mobile and security features) are more likely to be censored than females or users who are not verified. In addition, users from provinces such as Hong Kong, Macao, and Beijing are more heavily censored compared to any other province in China over the same period.
Montclair State University Digital Commons Citation
Kenney, Wayne and Leberknight, Christopher S., "User demographics and censorship on sina weibo" (2021). Department of Computer Science Faculty Scholarship and Creative Works. 781.
https://digitalcommons.montclair.edu/compusci-facpubs/781
Rights
CC BY-NC-ND 4.0
Published Citation
Kenney, W., & Leberknight, C. User Demographics and Censorship on Sina Weibo. Proceedings of the 54th Hawaii International Conference on System Sciences.