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)

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