Fake News Detection Using NLP and Machine Learning

Fake news has emerged as one of the most critical challenges in the digital information era, with the potential to mislead individuals, manipulate public opinion, and influence political and social decisions.With the rapid growth of social media platforms such as Facebook, Twitter, and WhatsApp, news spreads to millions of users within seconds, often without verification.The absence of editorial oversight and fact-checking mechanisms has made online users highly vulnerable to misinformation.This research paper presents a machine learningbased approach for detecting fake news by analyzing the textual content of news articles.The proposed system uses Natural Language Processing (NLP) techniques for text preprocessing and feature extraction, followed by supervised learning algorithms such as Naïve Bayes and Support Vector Machines (SVM) for classification.In addition, stance detection and sentiment analysis are incorporated to capture contextual and emotional patterns commonly associated with fake news.The system accepts a news article or URL as input and predicts whether the content is real or fake, along with a credibility indication.Experimental evaluation demonstrates that combining traditional text-based features with stance and sentiment information improves classification accuracy.The proposed approach aims to provide an efficient, scalable, and automated solution to combat the growing threat of fake news and promote responsible digital information consumption.