Fake News Detection Using Machine Learning and Natural Language Processor

Author(s)	Mr. Sohail Khan, Mr. Piyush Gautam, Prof. Priyanka Behera, Shivangi Bala
Country	India
Abstract	Today, the preservation of the species is a great challenge. This study introduces a machine learning system for automatically detecting fake news using language analysis tools. The system prepares text by breaking it into words(Tokens), removing common words, shortening words to their base forms, and turning text into numbers that show word importance. We tested five different methods—Logistic Regression, Multino-mial Naive Bayes, Random Forest, and Support Vector–based classifiers Machine (SVM), and Gradient Boosting—on 20,000 news articles. The results show that the best model achieved an accuracy of over 95 percent, with strong precision and recall across various news types. The system includes a simple web page that checks news in real-time and displays the level of confidence in the results.This study helps Community resources such as the local press and radio to verify information using computer methods.
Keywords	Fake News Detection, Machine Learning, Natu ral Language Processing, TF-IDF Vectorization, Text Classifica tion, Information Credibility
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2026
Published On	2026-06-16

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