Improving fake news classification using dependency grammar

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441615" target="_blank" >RIV/00216208:11320/21:10441615 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=X-VZP0.Gdh" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=X-VZP0.Gdh</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0256940" target="_blank" >10.1371/journal.pone.0256940</a>

Result language

angličtina

Original language name

Improving fake news classification using dependency grammar

Original language description

Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.

Czech name

—

Czech description

—

Type

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

CEP classification

—

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

2021

Confidentiality

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

Name of the periodical

PLoS One

ISSN

1932-6203

e-ISSN

—

Volume of the periodical

16

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

22

Pages from-to

e0256940

UT code for WoS article

000707052100010

EID of the result in the Scopus database

2-s2.0-85114874879