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.
MSU Digital Commons Citation
Leberknight, Christopher; Peng, Jing; Feldman, Anna; and Ng, Kei Yin, "Linguistic Characteristics of Censorable Language on SinaWeibo" (2018). Department of Computer Science Faculty Scholarship and Creative Works. 3.