Fake news detection using natural language processing

Fake news is rampart in the society of the current country and has caused the society to distrust the media, fake news is chosen over real news articles. As a result in the current paper, we discuss the application of natural language processing methods in the identification of fake news on the different platforms. Here we present an unobtrusive framework to automatically interpret news articles as real or fake based on supervised learning with the help of feature extraction from natural language processing. This method contains features based on textual elements which include syntactic features, semantic features and features that speaks about the sentiment of the given content in detail. We enhance feature representation by incorporating pre-trained word embeddings and transformer-based models such as BERT which relieves the amount of structural design and hence, results in better classification accuracy. The experiments we want to propose rely on a dataset containing a set of different news sources; to provide a more realistic scenario, when tuning the model, we use a balanced combination of real news and fake ones, using made up stories. To assess the effectiveness of the proposed framework, we employ tools for performance assessment, namely: precision, recall and F1 score to detect fake information. This result also shows the importance of context and shows how NLP techniques can distinguish between two forms of language use that typically define fake news. In addition, we explain how our work relates to prior research on media literacy and the creation of algorithms for journalists and non-journalists alike. Moreover, our research contributes to the stream of studies that aim at applying NLP for enhancing the effectiveness of combating fake news and the emergence of the society that is better equipped to function within the information logistics of the digital world. All in all, the present overall study focuses on whether advanced language processing tools remain a vital weapon in the ongoing fight against fake news in the current news landscape.