Detection of Fake News Through Natural Language Processing using Machine Learning and Deep Learning Techniques
Sumit Kureel
M. Tech , Dept of CSE, Goel Institute of Technology & Management, (AKTU), Lucknow, India
Dr. Brijesh Pandey
Associate Professor, Dept of CSE, Goel Institute of Technology & Management,(AKTU), Lucknow, India
Dr. Mahima Shankar Pandey
Assistant Professor ,Data Science ,Galgotia College of Engineering & Technology,(AKTU), Greater Noida, India
📌 DOI: https://doi.org/10.63920/tjths.52043
🔑 Keywords: LSTM; NLP; Bi-LSTM; TF - IDF
📅 Publication Date: 08 June 2026
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Abstract:
TThe proliferation of online platforms and social media has dramatically accelerated the spread of information—and misinformation—on a global scale. In this context, fake news—fabricated or deceptive content deliberately presented as genuine news—poses a grave threat to society, influencing public opinion, undermining trust, and even endangering lives. Extensive experiments on a benchmark news dataset demonstrate the effectiveness of our approach. Using an 80/20 train-test split and standard NLP preprocessing, our model achieved approximately 98% classification accuracy, with similarly high precision, recall, and F1-score. These results are comparable to state-of-the-art models in the literature (for example, an attention-enhanced Bi-LSTM achieved 97.66% accuracy in [3] and a regularized LSTM model achieved 98% in [26]) and significantly outperform baseline methods. Analysis of the training curves shows stable convergence (Fig. 1), and the confusion matrix indicates balanced detection of both classes. The LSTM’s ability to capture long-range context and semantic nuances is key to this performance. In summary, by integrating robust preprocessing, TF–IDF feature extraction, and a well-tuned LSTM classifier, our framework provides a powerful tool for automated fake-news detection, offering an effective countermeasure to the rapid spread of misinformation.
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📖 How to Cite
Sumit K., Brijesh P., and Mahima Shankar P.(2026). Detection of Fake News Through Natural Language Processing using Machine Learning and Deep Learning Techniques. TEJAS J. Technol. Humanit. Sci.,, Vol. 05, Issue 02. https://doi.org/10.63920/tjths.52042
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References
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