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Developing a fake news identification model with advanced deep language transformers for Turkish COVID-19 misinformation data

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dc.contributor.author Bozuyla, Mehmet
dc.contributor.author Ozcift, Akin
dc.date.accessioned 2023-01-09T21:09:43Z
dc.date.available 2023-01-09T21:09:43Z
dc.date.issued 2022
dc.identifier.issn 1300-0632
dc.identifier.issn 1303-6203
dc.identifier.uri https://doi.org/10.3906/elk-2106-55
dc.identifier.uri https://search.trdizin.gov.tr/yayin/detay/529538
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/46166
dc.description.abstract The massive use of social media causes rapid information dissemination that amplifies harmful messages such as fake news. Fake-news is misleading information presented as factual news that is generally used to manipulate public opinion. In particular, fake news related to COVID-19 is defined as 'infodemic' by World Health Organization. An infodemic is a misleading information that causes confusion which may harm health. There is a high volume of misinformation about COVID-19 that causes panic and high stress. Therefore, the importance of development of COVID-19 related fake news identification model is clear and it is particularly important for Turkish language from COVID-19 fake news identification point of view. In this article, we propose an advanced deep language transformer model to identify the truth of Turkish COVID-19 news from social media. For this aim, we first generated Turkish COVID-19 news from various sources as a benchmark dataset. Then we utilized five conventional machine learning algorithms (i.e. Naive Bayes, Random Forest, K-Nearest Neighbor, Support Vector Machine, Logistic Regression) on top of several language preprocessing tasks. As a next step, we used novel deep learning algorithms such as Long Short -Term Memory, Bi-directional Long-Short-Term-Memory, Convolutional Neural Networks, Gated Recurrent Unit and Bi-directional Gated Recurrent Unit. For further evaluation, we made use of deep learning based language transformers, i.e. Bi-directional Encoder Representations from Transformers and its variations, to improve efficiency of the proposed approach. From the obtained results, we observed that neural transformers, in particular Turkish dedicated transformer BerTURK, is able to identify COVID-19 fake news in 98.5% accuracy. en_US
dc.language.iso en en_US
dc.publisher Scientific Technical Research Council Turkey-Tubitak en_US
dc.relation.ispartof Turkish Journal Of Electrical Engineering And Computer Sciences en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject &nbsp en_US
dc.subject Infodemic en_US
dc.subject fake news en_US
dc.subject BerTURK en_US
dc.subject language transformers en_US
dc.subject machine learning en_US
dc.subject COVID-19 en_US
dc.subject Science en_US
dc.title Developing a fake news identification model with advanced deep language transformers for Turkish COVID-19 misinformation data en_US
dc.type Article en_US
dc.identifier.volume 30 en_US
dc.identifier.issue 3 en_US
dc.identifier.startpage 908 en_US
dc.identifier.endpage 926 en_US
dc.identifier.doi 10.3906/elk-2106-55
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.authorscopusid 57202919586
dc.authorscopusid 25654077600
dc.department-temp [Bozuyla, Mehmet] Pamukkale Univ, Fac Engn, Dept Elect Elect Engn, Denizli, Turkey; [Ozcift, Akin] Manisa Celal Bayar Univ, Hasan Ferdi Turgutlu Technol Fac, Dept Software Engn, Manisa, Turkey en_US
dc.identifier.scopus 2-s2.0-85128276542 en_US
dc.identifier.trdizinid 529538 en_US
dc.identifier.wos WOS:000774599800026 en_US

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