Measuring the Impact of Readability Features in Fake News Detection

Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, Carolina Scarton


Abstract
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.
Anthology ID:
2020.lrec-1.176
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1404–1413
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.176
DOI:
Bibkey:
Cite (ACL):
Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, and Carolina Scarton. 2020. Measuring the Impact of Readability Features in Fake News Detection. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1404–1413, Marseille, France. European Language Resources Association.
Cite (Informal):
Measuring the Impact of Readability Features in Fake News Detection (Santos et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.176.pdf