Fake news detection for the Russian language

Gleb Kuzmin, Daniil Larionov, Dina Pisarevskaya, Ivan Smirnov


Abstract
In this paper, we trained and compared different models for fake news detection in Russian. For this task, we used such language features as bag-of-n-grams and bag of Rhetorical Structure Theory features, and BERT embeddings. We also compared the score of our models with the human score on this task and showed that our models deal with fake news detection better. We investigated the nature of fake news by dividing it into two non-overlapping classes: satire and fake news. As a result, we obtained the set of models for fake news detection; the best of these models achieved 0.889 F1-score on the test set for 2 classes and 0.9076 F1-score on 3 classes task.
Anthology ID:
2020.rdsm-1.5
Volume:
Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
RDSM
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
45–57
Language:
URL:
https://aclanthology.org/2020.rdsm-1.5
DOI:
Bibkey:
Cite (ACL):
Gleb Kuzmin, Daniil Larionov, Dina Pisarevskaya, and Ivan Smirnov. 2020. Fake news detection for the Russian language. In Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), pages 45–57, Barcelona, Spain (Online). Association for Computational Linguistics.
Cite (Informal):
Fake news detection for the Russian language (Kuzmin et al., RDSM 2020)
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PDF:
https://aclanthology.org/2020.rdsm-1.5.pdf