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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Notes
- 1.
The dataset is available at https://github.com/jjsjunquera/UnmaskingBiasInNews.
- 2.
We use the BNC corpus (https://www.kilgarriff.co.uk/bnc-readme.html) for the extraction of the most frequent words as in [7].
- 3.
- 4.
In [5] the authors used \(n \in [1,3]\).
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Acknowledgments
The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31).
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Sánchez-Junquera, J., Rosso, P., Montes-y-Gómez, M., Ponzetto, S.P. (2023). Unmasking Bias in News. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_47
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