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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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Blocking-Resilient Communications in Information-Centric Networks Using Router Redirection
Information-centric network (ICN) designs are susceptible to censorship especially packet filtering based on content names. Previous works on censorship circumvention in ICN either have high processing times or use proxies that can be blocked easily by the censoring agents. We design a new censorship circumvention approach for ICN using router redirection that enables a client in a censored region to retrieve blocked content from a censored destination without the censoring agent detecting the use of a censorship circumvention tool. We conduct ndnSIM-based simulation experiments showing that our approach is practical with only a modest end-to-end delay overhead.
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- Award ID(s):
- 1719386
- PAR ID:
- 10183613
- Date Published:
- Journal Name:
- 2019 IEEE Globecom Workshops (GC Wkshps)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GCWkshps45667.2019.9024405
