Abstract
It is the time when a single false story can travel the globe in minutes, shaping opinions before anyone has a chance to verify it. Fake news is no longer just a nuisance — it actively destabilises democracies, fuels communal tensions, and erodes public trust in institutions. Manual fact-checking, as noble an effort as it is, simply cannot keep up with the flood of content produced every day on social media. This paper explores how Artificial Intelligence can step in to help. We built a Fake News Detection System using Natural Language Processing (NLP) and Machine Learning (ML) that reads a news article and decides whether it is real or fabricated. The system uses TF-IDF vectorization to convert text into meaningful numerical features, and a Passive Aggressive Classifier to make the final call. Trained on roughly 20,000 news records from Kaggle, the system reached 92% accuracy, 90% precision, and 91% recall — results that genuinely surprised us with how well a lightweight approach could perform. Beyond the numbers, this paper also digs into what existing research has missed and where future systems need to go.
Keywords
Fake News Detection, Artificial Intelligence, Natural Language Processing, Machine Learning, TF-IDF, Misinformation.
Citations
IRE Journals:
Dr. Amandeep Singh Arora, Swati Gupta, Dr. Shalu Tandon, Dr. Vikas Rao Vadi, Charanpreet Kaur; Dr. Asjad Usmani "Fake News AI-Based Detection System" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 13-17 https://doi.org/10.64388/IREV9I10-1715927
IEEE:
Dr. Amandeep Singh Arora, Swati Gupta, Dr. Shalu Tandon, Dr. Vikas Rao Vadi, Charanpreet Kaur; Dr. Asjad Usmani
"Fake News AI-Based Detection System" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1715927
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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