Unveiling Fake News: A Machine and Deep Learning Approach

The internet has made it simpler for people to obtain and share news and information from a variety of sources, but it also promotes the spread of false information. This paper presents a technique for identifying bogus news that employs deep learning and machine learning and is implemented in Python. Four separate models—Naive Bayes, Decision Tree, Random Forest, and Bidirectional Long Short-Term Memory—are used in the research for this aim. The method is based on disentangling false news from actual news by examining the linguistic characteristics of news pieces. The results emphasize the value of DL approaches in detecting false news by showing that the BILSTM model is the most successful in doing so with an accuracy rate of 93%. The suggested strategy has important ramifications for boosting news source trustworthiness and fostering media literacy.