Paper Title
Fake News Detection Using Machine Learning and Artificial Intelligence
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Registration ID: IJNRD_311016
Published ID: IJNRD2512093
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Keywords
Fake News, ML, AI, Natural Language Processing, Deep Learning, BERT
Abstract
The exponential growth of digital media platforms has revolutionized information dissemination, but it has also amplified the spread of misinformation and fake news. Fake news poses significant threats to democratic processes, public health, and social trust, making its detection a critical research challenge. Traditional rule-based approaches have proven inadequate due to the dynamic and evolving nature of deceptive content. Consequently, machine learning (ML) and artificial intelligence (AI) techniques have emerged as powerful tools for detecting and mitigating fake news. This study explores the application of ML and AI in fake news detection, focusing on text classification, natural language processing (NLP), and deep learning architectures. Classical ML algorithms such as Support Vector Machines, Naïve Bayes, and Random Forests have demonstrated effectiveness in identifying linguistic and stylistic features of misinformation. However, recent advances in deep learning, particularly recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models like BERT and RoBERTa, have significantly improved detection accuracy by capturing semantic, contextual, and syntactic nuances. Hybrid approaches that integrate metadata, social network analysis, and multimodal features further enhance robustness against adversarial manipulation. The paper also highlights challenges such as dataset bias, adversarial attacks, multilingual detection, and ethical concerns surrounding censorship and freedom of speech. Future directions emphasize explainable AI (XAI), federated learning for privacy-preserving detection, and real-time multimodal systems capable of analyzing text, images, and videos simultaneously. By leveraging ML and AI, fake news detection systems can evolve into scalable, adaptive, and transparent solutions, thereby strengthening information integrity in the digital age.
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How To Cite (APA)
Pranav Kumar, Raj Kumar Vishwakarma, Sumit Kumar, Pankaj Vishwakarma, & Binod Kumar (December-2025). Fake News Detection Using Machine Learning and Artificial Intelligence. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(12), a829-a833. https://ijnrd.org/papers/IJNRD2512093.pdf
Issue
Volume 10 Issue 12, December-2025
Pages : a829-a833
Other Publication Details
Paper Reg. ID: IJNRD_311016
Published Paper Id: IJNRD2512093
Research Area: Other area not in list
Author Type: Indian Author
Country: Ranchi, Jharkhand, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2512093.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2512093
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