AI-Based Fake News Detection Using Machine Learning

The rapid growth of online news and social media has made information widely accessible, but it has also increased the circulation of misleadingand fabricated content. Manual verification is time-consuming and may not scale to the volume of online information. This paper proposes anAI-Based Fake News Detection system using machine-learning techniques to classify news articles or claims as potentially fake or potentiallyreliable based on patterns learned from labelled examples. The workflow includes dataset collection, text preprocessing, feature extraction,model training, evaluation, and a user-facing prediction interface. Algorithms such as Logistic Regression, Multinomial Naive Bayes, andSupport Vector Machines can be evaluated as baseline models, depending on the dataset and experiment design. Predictions should be treated asscreening signals rather than definitive fact-checks, because performance can be affected by dataset bias, changing events, satire, and publisherdifferences. This paper presents a proposed methodology and evaluation plan; performance values must be measured from the implementedsystem.