NewsGuard: An intelligent system for detection of misinformation in news media

The active expansion of digital media, as well as social networking services, has raised the dispersion of false information and fake news to serious issues, which put pressure on society, its opinion, and democracy itself. Manual verification is not easy and efficient since fake news tend to spread more rapidly than real news because it is sensational. The current research provides NEWSGUARD, a smart system that is aimed at the automated fake news detection relying on a hybrid method of using machine learning and deep learning techniques. The suggested system proposes the use of Natural Language Processing (NLP) in processing of text and extracting features. Term Frequency-Inverse Document Frequency (TF-IDF) is used to create statistical features whereas semantic representations are retrieved with word embeddings. Various classification models are deployed, such as Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Convolutional Neural Networks (CNN) and Long short-term memory (LSTM). The hybrid model is created that combines the benefits of both machine learning and deep learning models and allows achieving better performance in classification. The experimental findings show that the hybrid model has a higher performance compared to the individual models where it has a high accuracy with a high precision, recall and F1 score. This research result demonstrates the usefulness of statistical and semantic features as a combination to detect fake news. The suggested NEWSGUARD solution offers the scalable, correct, and effective tool to fight with the misinformation, and it will be able to be further improved by using advanced models and real-time implementation.