Year: 2025
Type: article
Abstract: The proliferation of fake news on digital platforms poses a significant threat to public trust, democratic processes, and societal stability. With the increasing difficulty of distinguishing between authentic and fabricated content, there is a growing need for intelligent systems that can automatically identify deceptive information. This research presents a hybrid approach to fake news detection, combining Natural Language Processing (NLP) techniques with advanced Machine Learning (ML) models to enhance classification accuracy and reliability. The system extracts a range of linguistic, semantic, and metadata features-such as sentiment polarity, syntactic patterns, and source credibility-from a labeled dataset of real and fake news articles. Multiple classifiers, including Random Forests, Support Vector Machines, and Neural Networks, are trained and optimized using cross -validation techniques. To further improve performance, ensemble learning methods such as boosting and majority voting are employed. The final model is deployed through a web -based application, allowing users to input news content and receive explainable predictions about its authenticity. Experimental results demonstrate high accuracy, precision, and recall, validating the system's effectiveness in real -world scenarios. This work contributes a scalable and transparent solution to the ongoing challenge of online misinformation.
Authors: Gautham Murali, Rinsa Rees
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409843904