User Demographics and Censorship on Sina Weibo

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dc.contributor.authorKenney, Wayne
dc.contributor.authorLeberknight, Christopher
dc.date.accessioned2020-12-24T19:33:03Z
dc.date.available2020-12-24T19:33:03Z
dc.date.issued2021-01-05
dc.description.abstractThis 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.
dc.format.extent7 pages
dc.identifier.doi10.24251/HICSS.2021.330
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70944
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.titleUser Demographics and Censorship on Sina Weibo
prism.startingpage2709

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