Detecting Fake News based on Information Retrieval, NLP, Machine Learning, its Impact by Auto-Populating

In the era of rapid misinformation dissemination, the identification of fake news has become increasingly important. Our approach endeavors to develop a comprehensive approach for detecting fake news by analyzing three critical inputs: text, image, and URL. The framework comprises information retrieval, natural language processing, and machine learning. We employ web scraping techniques to collect pertinent data for analysis. It mainly goes through a four-layer checking process, which increases the probability of identifying and analyzing the patterns of the news and produce the most accurate results as compared to the existing methodologies