FAKE NEWS DETECTION USING PYTHON

Abstract

The proliferation of fake news in recent years has raised concerns about the trustworthiness and reliability of online information. To solve this problem, we propose a fake news detector that uses machine learning techniques to accurately classify news as true or false. The system collects a wide range of information from a variety of sources and prioritizes the information to ensure it is suitable for analysis. Features are then extracted from the text using methods such as TFIDF and word embedding to capture text that distinguishes between real and fake news. Machine learning models such as logistic regression or support vector machines are trained on domains to learn patterns and identify fake news. Evaluate the training model using performance metrics such as accuracy, precision, recall, and F1 score. The system provides a userfriendly interface that allows users to access newsletters and receive classification results in real time. Our test results show that the fake news detector has a clear distinction between real news and fake news. The aim is to provide users with reliable tools to combat fake news and promote the dissemination of legitimate information in the digital age.