Open Access

AI-Powered Fake News Detection Using Natural Language Processing

Volume 3, Issue 11

  • Author(s)Nakul Saraf, Girdhari Lal
  • AffiliationStudent, Department of Artificial Intelligence and Data Science Assistant Professor, Department of Artificial Intelligence and Data Science Poornima Institute of Engineering and Technology Jaipur, India Email: nakulsaraf8716@gmail.com,girdhari.lal@poornima.org ,
  • Page No.35-48
  • Volume, Issue & YearVolume 3 Issue 11,Nov-2026
  • Published On13/11/2026
  • JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
  • ISSN No.3048-9350
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Abstract

Automated detection of fake news, meaning fabricated news-style content that may circulate as misinformation or disinformation, is motivated by the speed and volume at which online content spreads and by the limited scalability of manual fact-checking. This paper treats fake-news detection as binary text classification and evaluates a hybrid model that fuses a fine-tuned BERT encoder, a convolutional branch operating over BERT token representations, and an auxiliary branch built from TF-IDF/SVD features and seven handcrafted linguistic features. The model is compared with Naive Bayes, logistic regression, a linear SVM, a word-embedding CNN, an LSTM and fine-tuned BERT on the WELFake dataset [1] (62,833 articles after cleaning; 55.36% real and 44.64% fake), using a stratified 80/10/10 split repeated with random seeds 42, 43 and 44. Over the three runs, the hybrid obtained mean test accuracy, precision, recall and F1-score of 99.62%, 99.64%, 99.51% and 99.58%, compared with 99.45%, 99.42%, 99.35% and 99.38% for fine-tuned BERT; its mean ROC-AUC (0.99968) was slightly lower than that of BERT (0.99984). No significance test was performed, and with three runs the differences should be interpreted cautiously. The principal limitation is that evaluation is restricted to a single benchmark, so robustness to unseen publishers, topics and time periods is untested. Ablation of the individual branches, cross-dataset evaluation, inference-latency measurement and prediction-level explanation are left for future work.

Keywords: Fake news detection, Natural language processing, BERT, Text classification, Deep learning, Misinformation, Linguistic features, WELFake.

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