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Unmasking Bias in News
June 11, 2019 ยท Declared Dead ยท ๐ Conference on Intelligent Text Processing and Computational Linguistics
"No code URL or promise found in abstract"
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Authors
Javier Sรกnchez-Junquera, Paolo Rosso, Manuel Montes-y-Gรณmez, Simone Paolo Ponzetto
arXiv ID
1906.04836
Category
cs.CL: Computation & Language
Citations
3
Venue
Conference on Intelligent Text Processing and Computational Linguistics
Last Checked
4 months ago
Abstract
We present experiments on detecting hyperpartisanship in news using a 'masking' method that allows us to assess the role of style vs. content for the task at hand. Our results corroborate previous research on this task in that topic related features yield better results than stylistic ones. We additionally show that competitive results can be achieved by simply including higher-length n-grams, which suggests the need to develop more challenging datasets and tasks that address implicit and more subtle forms of bias.
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