Neural Network Prediction of Censorable Language

Kei Yin Ng, Anna Feldman, Jing Peng, Chris Leberknight


Abstract
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.
Anthology ID:
W19-2105
Volume:
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | NLP+CSS | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
40–46
Language:
URL:
https://aclanthology.org/W19-2105
DOI:
10.18653/v1/W19-2105
Bibkey:
Cite (ACL):
Kei Yin Ng, Anna Feldman, Jing Peng, and Chris Leberknight. 2019. Neural Network Prediction of Censorable Language. In Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science, pages 40–46, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Neural Network Prediction of Censorable Language (Ng et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2105.pdf