Masking and Transformer-based Models for Hyperpartisanship Detection in News

Javier Sánchez-Junquera, Paolo Rosso, Manuel Montes-y-Gómez, Simone Paolo Ponzetto


Abstract
Hyperpartisan news show an extreme manipulation of reality based on an underlying and extreme ideological orientation. Because of its harmful effects at reinforcing one’s bias and the posterior behavior of people, hyperpartisan news detection has become an important task for computational linguists. In this paper, we evaluate two different approaches to detect hyperpartisan news. First, a text masking technique that allows us to compare style vs. topic-related features in a different perspective from previous work. Second, the transformer-based models BERT, XLM-RoBERTa, and M-BERT, known for their ability to capture semantic and syntactic patterns in the same representation. Our results corroborate previous research on this task in that topic-related features yield better results than style-based ones, although they also highlight the relevance of using higher-length n-grams. Furthermore, they show that transformer-based models are more effective than traditional methods, but this at the cost of greater computational complexity and lack of transparency. Based on our experiments, we conclude that the beginning of the news show relevant information for the transformers at distinguishing effectively between left-wing, mainstream, and right-wing orientations.
Anthology ID:
2021.ranlp-1.140
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1244–1251
Language:
URL:
https://aclanthology.org/2021.ranlp-1.140
DOI:
Bibkey:
Cite (ACL):
Javier Sánchez-Junquera, Paolo Rosso, Manuel Montes-y-Gómez, and Simone Paolo Ponzetto. 2021. Masking and Transformer-based Models for Hyperpartisanship Detection in News. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1244–1251, Held Online. INCOMA Ltd..
Cite (Informal):
Masking and Transformer-based Models for Hyperpartisanship Detection in News (Sánchez-Junquera et al., RANLP 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.ranlp-1.140.pdf