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Unmasking Deception: A Comprehensive Survey on Fake News Detection Strategies and Technologies


Affiliations
1 Department of Computer Science, American International University-Bangladesh, Bangladesh
 

Fake news threatens public debate and decision-making in a digital age. This comprehensive paper, "Unmasking Deception," methodically covers false news detecting tactics and technology. We summarize a wide range of study results, methods, and technological advances to give a thorough overview of disinformation detection and mitigation. Our research covers linguistic, content-based, machine learning, and deep learning false news identification. We examine emerging misleading strategies and propose novel remedies using natural language processing, network analysis, and other innovative methods. In addition, we evaluate current detection systems in real-world circumstances and address the ethical implications of their use. The findings of the research help scholars, policymakers, and technology developers understand false news and advance the area. The primary objective is to enhance the safeguarding of the information environment against misinformation by a critical evaluation of existing methodologies.

Keywords

false, detection, digital, debate, decision-making, ecosystem.
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  • Unmasking Deception: A Comprehensive Survey on Fake News Detection Strategies and Technologies

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Authors

Md. Faruk Abdullah AL Sohan
Department of Computer Science, American International University-Bangladesh, Bangladesh
Nusrat Jahan Trisna
Department of Computer Science, American International University-Bangladesh, Bangladesh
Rahul Das Joy
Department of Computer Science, American International University-Bangladesh, Bangladesh

Abstract


Fake news threatens public debate and decision-making in a digital age. This comprehensive paper, "Unmasking Deception," methodically covers false news detecting tactics and technology. We summarize a wide range of study results, methods, and technological advances to give a thorough overview of disinformation detection and mitigation. Our research covers linguistic, content-based, machine learning, and deep learning false news identification. We examine emerging misleading strategies and propose novel remedies using natural language processing, network analysis, and other innovative methods. In addition, we evaluate current detection systems in real-world circumstances and address the ethical implications of their use. The findings of the research help scholars, policymakers, and technology developers understand false news and advance the area. The primary objective is to enhance the safeguarding of the information environment against misinformation by a critical evaluation of existing methodologies.

Keywords


false, detection, digital, debate, decision-making, ecosystem.

References