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Arabic fake news detection in social media Based on AraBERT
21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022 ; : 214-220, 2022.
Article in English | Scopus | ID: covidwho-2321950
ABSTRACT
Social media has become a source of information for many people because of its freedom of use. As a result, fake news spread quickly and easily, regardless of its credibility, especially over the past decade. The vast amount of information being shared has fraudulent practices that negatively affect readers' cognitive abilities and mental health. In this study, we aim to introduce a new Arabic COVID-19 dataset for fake news related to COVID-19 from Twitter and Facebook. Afterward, we applied two pre-Trained models of classification AraBERT and BERT base Arabic. As a result, AraBERT models obtained better accuracy than BERT base Arabic in two datasets. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022 Year: 2022 Document Type: Article