Application of deep learning algorithms detecting fake and correct textual or verbal news
The ongoing spread and expansion of information technology and social media sites has made it easier for people to access different types of news - political, economic, medical, social etc. - through these platforms. This rapid growth in news outlets and the demand for information has blurred the lines between real and fake news, and led to the dissemination of fake news, which is a dangerous state of affairs. The outbreak of the coronavirus pandemic and a rising awareness of the dangers posed all across the globe saw a parallel rise in fake news and rumors, as like as unsubstantiated statements and deceptive ideas. The main aim of this study is supposed to set out to overcome these kind of problems in the future, with application of deep learning algorithms (LSTM, Bi-LSTM, BERT), using a large dataset (39279 rows) to identify fake and correct textual or verbal news. The results of the deep learning application using different algorithms show that the BERT model performed the best, achieving a text classification accuracy of 96.63 %.
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Hivatkozás
MLA: Dadvandipour, Samad, and Yahya Layth Khaleel. "Application of deep learning algorithms detecting fake and correct textual or verbal news." Production Systems and Information Engineering 10.2 (2022): 37-51.
APA: Dadvandipour, S., & Khaleel, Y. L. (2022). Application of deep learning algorithms detecting fake and correct textual or verbal news. Production Systems and Information Engineering, 10(2), 37-51.
ISO690: DADVANDIPOUR, Samad; KHALEEL, Yahya Layth. Application of deep learning algorithms detecting fake and correct textual or verbal news. Production Systems and Information Engineering, 2022, 10.2: 37-51.
BibTeX:
@article{dadvandipour2022application,
title={Application of deep learning algorithms detecting fake and correct textual or verbal news},
author={Dadvandipour, Samad and Khaleel, Yahya Layth},
journal={Production Systems and Information Engineering},
volume={10},
number={2},
pages={37--51},
year={2022}
}