Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can Self-Censorship in News Media be Detected Algorithmically? A Case
  Study in Latin America   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.

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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: econ
30
The categories of this paper are: q-alg
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The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin