Fake News Detection System
Year: 2025
Type: article
Abstract: In today’s digital age, the rapid spread of fake news poses a serious challenge to society, media platforms, and individual trust. This project presents a Fake News Detection System that leverages machine learning and natural language processing (NLP) techniques to automatically identify and classify news articles as real or fake. The system is designed with a web-based interface, making it easy for users to input text or articles and instantly receive predictions. By training on a large dataset of news articles, the model learns linguistic patterns and deceptive writing styles often associated with misinformation.The proposed system not only improves accuracy in detecting fake news but also provides a scalable solution that can be integrated into social media platforms, news aggregators, or content verification tools. Through this project, we aim to contribute to the fight against misinformation, helping individuals make more informed decisions while consuming digital content. Ultimately, the Fake News Detection System bridges the gap between technology and truth, ensuring that credible information prevails in the digital world.
Author: Jashwanth Ettamalla, Rachamudi Victor Deevan, Alphonsa Mandla
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7079666647
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