Uncovering the Truth: Exploring Traditional Deep Learning Techniques for Fabricated News Detection

The identification of fabricated news is becoming increasingly critical in this time of social media and the internet, where it can propagate fast and cause significant damage. Recent research has focused on NLP and ML methods to detect fake news. This research study investigates traditional deep learning techniques such as Lightning Module, Logistic Regression, LSTM, Word Embedding, RNN, and Bag of Ngrams for detecting Fabricated news. The working of these techniques is evaluated on several benchmark datasets, and their accuracy and efficiency are compared. Moreover, the approach towards prediction only by the news articles is discussed. Results indicate that the approach using these techniques can effectively detect fake news with high accuracy, providing a promising solution to this complex problem. This study aims to achieve the improvement of more advanced and effective techniques for Identifying fake news, helping to mitigate its side effects on society.