Comparison of fake and real news based on morphological analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F20%3A39916682" target="_blank" >RIV/00216275:25410/20:39916682 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1877050920312394" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050920312394</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2020.04.247" target="_blank" >10.1016/j.procs.2020.04.247</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of fake and real news based on morphological analysis

Popis výsledku v původním jazyce

Easy access to information results in the phenomenon of false news spreading intentionally through social networks to manipulate people&apos;s opinions. Fake news detection has recently attracted growing interest from the general public and researchers. The paper deals with the morphological analysis of two datasets containing 28 870 news articles. The results were verified using the third dataset consisting of 402 news articles. The analysis of the datasets was carried out using lemmatization and POS tagging. The morphological analysis as a process of classifying the words into grammatical-semantic classes and assigning grammatical categories to these words. Individual words from articles were annotated and statistically significant differences were examined between the classes found in fake and real news articles. The results of the analysis show that statistically significant differences are mainly in the verbs and nouns word classes. Finding statistically significant differences in individual categories of word classes is an important piece of information for the future fake news classifier in terms of selecting appropriate variables for the classification.

Název v anglickém jazyce

Comparison of fake and real news based on morphological analysis

Popis výsledku anglicky

Easy access to information results in the phenomenon of false news spreading intentionally through social networks to manipulate people&apos;s opinions. Fake news detection has recently attracted growing interest from the general public and researchers. The paper deals with the morphological analysis of two datasets containing 28 870 news articles. The results were verified using the third dataset consisting of 402 news articles. The analysis of the datasets was carried out using lemmatization and POS tagging. The morphological analysis as a process of classifying the words into grammatical-semantic classes and assigning grammatical categories to these words. Individual words from articles were annotated and statistically significant differences were examined between the classes found in fake and real news articles. The results of the analysis show that statistically significant differences are mainly in the verbs and nouns word classes. Finding statistically significant differences in individual categories of word classes is an important piece of information for the future fake news classifier in terms of selecting appropriate variables for the classification.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Procedia Computer Science : Third International Conference on Computing and Network Communications (CoCoNet&apos;19)

ISBN

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ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

9

Strana od-do

2285-2293

Název nakladatele

Elsevier Science BV

Místo vydání

Amsterdam

Místo konání akce

Trivadrum

Datum konání akce

18. 12. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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