Identifying COVID-19 Fake News on Social Networks Using Deep Learning: You will not know what happens next!

Fake News has long been an influential source of information on social media, misleading people and causing unpredictable consequences, especially in the COVID-19 era when the spread of Fake News has been amplified. There is a need to develop a platform that can detect Fake News on social platforms. With the development of Natural Language Processing (NLP) and Deep Learning, the detection of misleading information is becoming a reality. In this paper, we propose a feasible optimization scheme that combines and optimizes NLP and Deep Learning to improve the accuracy of Fake News detection. Our model achieves a hit rate of up to 95.3% compared to state-of-the-art techniques. In the proposed system, a GUI-based interface is also designed and developed to facilitate news detection.

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Publication Details

Journal
WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS
Published
2024-10-10
DOI
https://doi.org/10.37394/23207.2024.21.160
Citations
1
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
1.13
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article

Identifying COVID-19 Fake News on Social Networks Using Deep Learning: You will not know what happens next!

Pushpendu Kar, Zhongyi Wang
1 citations
WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS
Misinformation and Its Impacts
1.13
article

Identifying COVID-19 Fake News on Social Networks Using Deep Learning: You will not know what happens next!

Pushpendu Kar, Zhongyi Wang
article en
1 citations

Abstract

Fake News has long been an influential source of information on social media, misleading people and causing unpredictable consequences, especially in the COVID-19 era when the spread of Fake News has been amplified. There is a need to develop a platform that can detect Fake News on social platforms. With the development of Natural Language Processing (NLP) and Deep Learning, the detection of misleading information is becoming a reality. In this paper, we propose a feasible optimization scheme that combines and optimizes NLP and Deep Learning to improve the accuracy of Fake News detection. Our model achieves a hit rate of up to 95.3% compared to state-of-the-art techniques. In the proposed system, a GUI-based interface is also designed and developed to facilitate news detection.

WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICSVol. 21
University of Nottingham Ningbo China (CN)
Openalex Percentile: Top 16%
Misinformation and Its Impacts
1.13
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