  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 detecting (self) 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 self censorship events.
