Fake News Detection using NLP Techniques

Language analysis is used in fake news identification utilising natural language processing (NLP) approaches to separate fact from fiction. In order to prepare the data for analysis, the procedure begins with data collection, which includes labelling a set of actual and fake news articles. Next, text preparation is done, such as tokenisation and stop-word removal. Using techniques like TF-IDF or word embeddings (e.g., Word2Vec, BERT), key elements like word frequencies, sentiment, and linguistic style are extracted, identifying underlying patterns that may point to erroneous reporting. These characteristics are used to train machine learning algorithms, such as Support Vector Machines, Random Forests, and deep learning models like LSTM and transformers, to identify biassed viewpoints, overblown language, and inconsistent content that are common in fake news. Once trained, these algorithms are able to forecast the probability that fresh articles would be fraudulent, which helps