Leveraging NLP and Social Network Analytic Techniques to Detect Censored Keywords: System Design and Experiments

Date
2019-01-08
Authors
Leberknight, Chris
Feldman, Anna
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Abstract
Internet regulation in the form of online censorship and Internet shutdowns have been increasing over recent years. This paper presents a natural language processing (NLP) application for performing cross country probing that conceals the exact location of the originating request. A detailed discussion of the application aims to stimulate further investigation into new methods for measuring and quantifying Internet censorship practices around the world. In addition, results from two experiments involving search engine queries of banned keywords demonstrates censorship practices vary across different search engines. These results suggest opportunities for developing circumvention technologies that enable open and free access to information.
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Dark Digital Government: Exploring the Dangers — Issues, Concerns, and Negative Impacts, Digital Government, Censorship, Digital Democracy, Natural Language Processing, Security, Social Network Analysis
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