Abstract
This study examines how Chinese authorities distinguish between acceptable and unacceptable diplomatic discourse within China’s cyberspace. Using computer-assisted text analysis, we analyze a unique dataset comprising US Embassy social media posts on the Chinese platform Weibo between 2010 and 2020. Our findings challenge prevailing assumptions, revealing a degree of tolerance for posts that promote Western social life, human rights, and liberal democracy. Instead, the focus of the Chinese authorities is to curtail the US Embassy’s public engagement in discussing politically sensitive topics, particularly those related to China’s regime security. We contend that a hierarchical security framework can provide a more thorough understanding of China’s information control practices. This study extends previous research on China’s information control beyond the domestic context and provides a fresh examination of China’s domestic politics and foreign policy. It also highlights the potential and limitations of foreign public diplomacy in China’s cyberspace.
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Notes
According to a report by the Australian Strategic Policy Institute in 2018, 64% of censorship cases of foreign embassies’ accounts that occurred between November 2017 and January 2018 consisted of disabling the comment function on unfavorable posts.
The jiebaR package is available at https://cran.r-project.org/web/packages/jiebaR/index.html. Jieba’s algorithm is capable of accurate text segmentation. However, the US Embassy uses various Chinese translations when referring to certain political personalities, which may render jieba unable to recognize their names. For example, President Obama is sometimes translated to “欧巴马” by the Weibo account of the US Embassy, while the Chinese official translation is “奥巴马”, a homonym of “欧巴马”. We thus manually added some synonyms, such as “欧巴马”, as customized new words to ensure the automatic segmentation was done correctly.
Stop-words are unnecessary words that should be removed in natural language processing. The Baidu stop-words, which are included in the quanteda package, are a list of meaningless Chinese words (such as “是”, “的”, and “有”) widely used in Chinese text analysis.
DTM is a matrix whose rows are documents and columns are terms. A 26,778 *58,022 DTM means there are 26,778 Weibo posts with 58,778 unique Chinese words in our corpus.
Covariates of textual data refers to any variable associated with a document. For Weibo data, the covariates could be posting time, author, number of replies, etc.
We conducted an analysis to estimate the proportion of each topic and examined the change in topic proportion over a 10-year period, which we presented in Appendix D.
We also applied the STM to our data on a yearly basis as a robustness check, with results detailed in Appendix F. These findings show that topics regarding US-China elite politics are always more likely to be censored than other topics, which is consistent with our main result.
The X2 values of terms in commendable Weibo posts are signed negative for reference. A detailed explanation of keyness analysis can be found in Appendix C.
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Acknowledgements
The authors express their gratitude to the three anonymous reviewers and editors of the Journal of Chinese Political Science; the workshop of Chinese Foreign Policy held at University of Nevada, Reno for their valuable comments on this article. They also thank Ja Ian Chong, Allison Evans, Ian Hartshorn, Todd Hall, Haifeng Huang, Taiyi Sun, Robert Ostergard, Taiyi Sun, and Min Ye, for their helpful suggestions. Authors also thank the excellent research assistance of Guo Li.
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Pu, X., Wang, C. & Zhou, Y. Censor and Sensitivity: How China Handles US Embassy’s Public Diplomacy in Chinese Cyber Space. J OF CHIN POLIT SCI (2023). https://doi.org/10.1007/s11366-023-09868-w
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DOI: https://doi.org/10.1007/s11366-023-09868-w