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Project Information

SATC: CORE: MEDIUM: COLLABORATIVE: A LINGUISTICALLY-INFORMED APPROACH FOR MEASURING AND CIRCUMVENTING INTERNET CENSORSHIP

Agency:
NSF

National Science Foundation

Project Number:
1704113
Contact PI / Project Leader:
LEBERKNIGHT, CHRIS S
Awardee Organization:
MONTCLAIR STATE UNIVERSITY STUDENT GOVERNMENT

Description

Abstract Text:
Internet censorship consists of 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. However, members of the Internet Freedom community lack comprehensive real-time awareness of where and how censorship is being imposed. The challenges to achieving such a solution include but are not limited to coverage, scalability, adoption, and safety. The project explores a linguistically-informed approach for measuring and circumventing Internet censorship.The research takes a new perspective on the problem by investigating a hybrid method for censorship detection and evasion from the lens of linguistic analysis. The team develops new models to measure Internet censorship, investigates mechanisms to circumvent censorship using linguistic techniques, conducts communication and social network measurements of censored content. Active Sensing and natural language processing techniques, in conjunction with machine learning and optimization, invigorates new research directions in Internet Freedom and produces new high quality data and tools available for public use. This new allogamy between computer science, information security, network analysis and linguistics provides the foundation for evolution of anti-censorship technologies. The research contributes to a number of fields including Internet censorship, privacy and online information retrieval, as well as computational social science by modeling and analyzing the phenomenon of censorship using the signal available in language. The broader contribution includes wide dissemination of the research results via peer-reviewed publications, special topic courses and workshops. Additional benefits include providing graduate and undergraduate researchers with significant experience of highly practical work on a difficult interdisciplinary problem. Significant gains are obtained in recruitment of minority students through research training in computer science and linguistics.
Project Terms:
Adoption; Awareness; censorship; Communication; Communities; computer science; Data Quality; Data Security; Detection; dissemination research; Educational workshop; Evolution; experience; Foundations; Freedom; Hybrids; Information Retrieval; Internet; Language; lens; Linguistics; Machine Learning; Measurement; Measures; member; Methods; Minority Recruitment; Modeling; Names; Natural Language Processing; Pathway Analysis; Peer Review; Privacy; Publications; Reporting; Research; research data dissemination; Research Personnel; Research Training; Safety; Signal Transduction; Social Network; Social Sciences; Student recruitment; Techniques; Technology; Time; tool; Work

Details

Contact PI / Project Leader Information:
Name:  LEBERKNIGHT, CHRIS S
Other PI Information:
FELDMAN, ANNA
Awardee Organization:
Name:  MONTCLAIR STATE UNIVERSITY STUDENT GOVERNMENT
City:  MONTCLAIR    
Country:  UNITED STATES
Congressional District:
State Code:  NJ
District:  11
Other Information:
Fiscal Year: 2017
Award Notice Date: 14-Aug-2017
DUNS Number: 053506184
Project Start Date: 15-Aug-2017
Budget Start Date:
CFDA Code: 47.070
Project End Date: 31-Jul-2020
Budget End Date:
Agency: ?

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National Science Foundation
Project Funding Information for 2017:
Year Agency

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

FY Total Cost
2017 NSF

National Science Foundation

$329,352

Results

i

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