November 18, 2019

New Research Article: “Social Media as a Sensor for Censorship Detection in News Media”

The following paper was recently shared by the authors on arXiv.

Title

Social Media as a Sensor for Censorship Detection in News Media

Author

Rongrong Tao
Virginia Tech

Baojian Zhou
SUNY, Albany

Adil Alim
SUNY, Albany

Feng Chen
SUNY, Albany

David Mares
University of California at San Diego

Patrick Butler
Virginia Tech

Naren Ramakrishnan
Virginia Tech

Source

via arXiv

Abstract

Censorship in social media has been well studied and provides insight into how governments stifle freedom of expression online. Comparatively less (or no) attention has been paid to censorship in traditional media (e.g., news) using social media as a bellweather. We present a novel unsupervised approach that views social media as a sensor to detect censorship in news media wherein statistically significant differences between information published in the news media and the correlated information published in social media are automatically identified as candidate censored events. We develop a hypothesis testing framework to identify and evaluate censored clusters of keywords, and a new near-linear-time algorithm (called GraphDPD) to identify the highest scoring clusters as indicators of censorship. We outline extensive experiments on semi-synthetic data as well as real datasets (with Twitter and local news media) from Mexico and Venezuela, highlighting the capability to accurately detect real-world censorship events.

Direct to Full Text (10 pages; PDF)

Gary Price About Gary Price

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

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