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.
Original language | English |
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Pages (from-to) | 1973-1987 |
Number of pages | 15 |
Journal | WSEAS Transactions on Business and Economics |
Volume | 21 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- Covid-19
- Deep Learning
- Fake News
- NLP
ASJC Scopus subject areas
- Economics and Econometrics
- Strategy and Management
- Organizational Behavior and Human Resource Management
- Marketing