Fake News Spreaders Detection: Sometimes Attention Is Not All You Need

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AE72T8I8S" target="_blank" >RIV/00216208:11320/22:E72T8I8S - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2078-2489/13/9/426" target="_blank" >https://www.mdpi.com/2078-2489/13/9/426</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/info13090426" target="_blank" >10.3390/info13090426</a>

Result language

angličtina

Original language name

Fake News Spreaders Detection: Sometimes Attention Is Not All You Need

Original language description

Guided by a corpus linguistics approach, in this article we present a comparative evaluation of State-of-the-Art (SotA) models, with a special focus on Transformers, to address the task of Fake News Spreaders (i.e., users that share Fake News) detection. First, we explore the reference multilingual dataset for the considered task, exploiting corpus linguistics techniques, such as chi-square test, keywords and Word Sketch. Second, we perform experiments on several models for Natural Language Processing. Third, we perform a comparative evaluation using the most recent Transformer-based models (RoBERTa, DistilBERT, BERT, XLNet, ELECTRA, Longformer) and other deep and non-deep SotA models (CNN, MultiCNN, Bayes, SVM). The CNN tested outperforms all the models tested and, to the best of our knowledge, any existing approach on the same dataset. Fourth, to better understand this result, we conduct a post-hoc analysis as an attempt to investigate the behaviour of the presented best performing black-box model. This study highlights the importance of choosing a suitable classifier given the specific task. To make an educated decision, we propose the use of corpus linguistics techniques. Our results suggest that large pre-trained deep models like Transformers are not necessarily the first choice when addressing a text classification task as the one presented in this article. All the code developed to run our tests is publicly available on GitHub.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Information (Switzerland)

ISSN

2078-2489

e-ISSN

2078-2489

Volume of the periodical

13

Issue of the periodical within the volume

9

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

1-22

UT code for WoS article

000859436100001

EID of the result in the Scopus database

2-s2.0-85138690007