Semantic graph based topic modelling framework for multilingual fake news detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ARKWJNVDH" target="_blank" >RIV/00216208:11320/23:RKWJNVDH - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2666651023000062" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2666651023000062</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.aiopen.2023.08.004" target="_blank" >10.1016/j.aiopen.2023.08.004</a>
Alternative languages
Result language
angličtina
Original language name
Semantic graph based topic modelling framework for multilingual fake news detection
Original language description
"Fake news detection is one of the most alluring problems that has grabbed the interest of Machine Learning (ML) and Natural Language Processing (NLP) experts in recent years. The majority of existing studies on detecting fake news are written in English, restricting its application outside the English-speaking population."
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
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)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"AI Open"
ISSN
2666-6510
e-ISSN
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Volume of the periodical
4
Issue of the periodical within the volume
2023
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
33-41
UT code for WoS article
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EID of the result in the Scopus database
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