This study provides preliminary insights into the linguistic features that contribute to Internet censorship in mainland China. We collected a corpus of 344 censored and uncensored microblog posts thatwere published onSinaWeibo and built a Naive Bayes classifier based on the linguistic, topic-independent, features. The classifier achieves a 79.34%accuracy in predicting whether a blog post would be censored on Sina Weibo.
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Neural Network Prediction of Censorable Language
Internet censorship imposes restrictions on what information can be publicized or viewed
on the Internet. According to Freedom House’s annual Freedom on the Net report,
more than half the world’s Internet users now live in a place where the Internet is censored
or restricted. China has built the world’s most extensive and sophisticated online censorship
system. In this paper, we describe a new corpus of censored and uncensored social media
tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts
that mention ‘sensitive’ topics or authored by ‘sensitive’ users. We use this corpus to build
a neural network classifier to predict censorship. Our model performs with a 88.50% accuracy
using only linguistic features. We discuss these features in detail and hypothesize
that they could potentially be used for censorship circumvention.
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- Award ID(s):
- 1704113
- NSF-PAR ID:
- 10463459
- Date Published:
- Journal Name:
- Proceedings of the 3rd Workshop on NLP and Computational Social Science (NLP+CSS) held in conjunction with NAACL 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
