@inproceedings{kuzmin-etal-2020-fake,
title = "Fake news detection for the {R}ussian language",
author = "Kuzmin, Gleb and
Larionov, Daniil and
Pisarevskaya, Dina and
Smirnov, Ivan",
booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.rdsm-1.5",
pages = "45--57",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kuzmin-etal-2020-fake">
<titleInfo>
<title>Fake news detection for the Russian language</title>
</titleInfo>
<name type="personal">
<namePart type="given">Gleb</namePart>
<namePart type="family">Kuzmin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniil</namePart>
<namePart type="family">Larionov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dina</namePart>
<namePart type="family">Pisarevskaya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="family">Smirnov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-dec</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">kuzmin-etal-2020-fake</identifier>
<location>
<url>https://aclanthology.org/2020.rdsm-1.5</url>
</location>
<part>
<date>2020-dec</date>
<extent unit="page">
<start>45</start>
<end>57</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Fake news detection for the Russian language
%A Kuzmin, Gleb
%A Larionov, Daniil
%A Pisarevskaya, Dina
%A Smirnov, Ivan
%S Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F kuzmin-etal-2020-fake
%X 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.
%U https://aclanthology.org/2020.rdsm-1.5
%P 45-57
Markdown (Informal)
[Fake news detection for the Russian language](https://aclanthology.org/2020.rdsm-1.5) (Kuzmin et al., RDSM 2020)
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.