Automatic Fake News Detection using AI/ML and Natural Processing Language

The growth of online media has changed how information is shared.However, it has also speed up the spread of misinformation and fake news.This kind of misleading content harms public trust, disrupts social harmony, and poses risks to democratic systems.This study introduces a Fake News Detection Application that uses machine learning (ML) and natural language processing (NLP) techniques to identify and classify both text and video content as either authentic or misleading.The system includes thorough preprocessing steps, such as tokenization, removing stop words, lemmatization, and feature extraction through TF-IDF and word embedding methods.The application features a user-friendly interface that combines these analytical models.Users can enter text or video URLs and get real-time assessments of authenticity, complete with confidence indicators.To ensure transparency, the system marks key linguistic and contextual features that affect the model's predictions.Experimental tests on datasets like LIAR and FakeNewsNet show that this approach reliably distinguishes between true and false information with high accuracy.Additionally, a feedback-based learning system allows the model to improve continuously through user interaction and retraining.This work adds to the ongoing research in automated misinformation detection by providing a scalable, easyto-understand, and interactive tool that helps users, journalists, and policymakers fight the negative impacts of fake news and enhance the credibility of digital communication.