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User Demographics and Censorship on Sina Weibo

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Title:User Demographics and Censorship on Sina Weibo
Authors:Kenney, Wayne
Leberknight, Christopher
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
Date Issued:05 Jan 2021
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.
Pages/Duration:7 pages
URI:http://hdl.handle.net/10125/70944
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.330
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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