Fake News detection using NLP and Ensemble Machine Learning

The development of the internet has resulted in an increased number of online news platforms, which has raised issues related to the rapid spread of fake news. Misinformation may affect people's opinions and beliefs and cause instability in various sectors such as the economy, politics, and social welfare. Manual verification and conventional techniques are inefficient approaches to fake news detection. The combination of artificial intelligence and natural language processing offers a highly efficient approach to detecting fake news. The latter involves techniques that extract meaningful information, linguistic characteristics, and contextual details from news sources and statements. Artificial intelligence improves the detection process through the development of machine learning algorithms that learn from huge volumes of news data and classify it into true and fake. The present research explores an AI-based fake news detection technique that utilizes natural language processing and machine learning algorithms. It describes the operational methodology of the system in terms of data preparation, extraction of TF-IDF features, and classification of news using different models, including Logistic Regression, XGBoost, LightGBM, and Voting Ensemble. In addition, the paper analyzes the performance indicators, benefits, drawbacks, and future potential of the proposed system.