Linguistic Characteristics of Censorable Language on SinaWeibo

Abstract

This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability.

References in corpus (3)

• Efficient Estimation of Word Representations in Vector Space
• Estimating Continuous Distributions in Bayesian Classifiers
• Defeating Image Obfuscation with Deep Learning

Cited by in corpus (1)

• Assessing Post Deletion in Sina Weibo: Multi-modal Classification of Hot Topics