TY - GEN
T1 - User demographics and censorship on sina weibo
AU - Kenney, Wayne
AU - Leberknight, Christopher S.
N1 - Funding Information:
The dataset in this paper is from the Open Weiboscope Data Access [15] project that is funded by the University of Hong Kong Seed Funding Program for Basic Research. This project’s aim is to make publicly available censored posts from selected microbloggers in order to promote a free internet and academic research. The authors of this paper have no association with this organization, and did not personally collect the data.
Funding Information:
The work is supported by the National Science Foundation under Grant No.: 1704113, Division of Computer and Networked Systems, Secure Trustworthy Cyberspace (SaTC).The data processing cluster was supported by the National Science Foundation under Grant No. CNS 1625636.
Publisher Copyright:
© 2021 IEEE Computer Society. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85108321908&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85108321908
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 2709
EP - 2715
BT - Proceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
A2 - Bui, Tung X.
PB - IEEE Computer Society
T2 - 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
Y2 - 4 January 2021 through 8 January 2021
ER -