Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
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.
User Demographics and Censorship on Sina Weibo
Online
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.
https://aisel.aisnet.org/hicss-54/dsm/data_mining/6