Detecting Censorable Content on Sina Weibo: A Pilot Study
Detecting Censorable Content on Sina Weibo: A Pilot Study
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DOI：
10.1145/3200947.3201037
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Kei Yin Ng;Anna Feldman;C. Leberknight
中科院分区：
文献类型：
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作者：
Kei Yin Ng;Anna Feldman;C. Leberknight
本研究对中国大陆网络审查的语言特征进行了初步的探讨。我们收集了344个新浪微博上发布的经过审查和未经审查的微博帖子的语料库，并基于语言，主题无关的特征构建了一个朴素贝叶斯分类器。该分类器在预测新浪微博上的博客文章是否会被审查方面达到了79.34%的准确率。
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 that were published on Sina Weibo 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.