Fake News Detection Using Machine Learning and Python

The rapid growth of online media has led to the widespread dissemination of information, making fake news detection an urgent challenge for society. Fake news has the potential to mislead individuals, influence public opinion, and cause serious social, political, and economic harm. This research aims to design a fake news detection system using machine learning techniques implemented in Python. The proposed methodology includes preprocessing text data, applying natural language processing (NLP) techniques such as tokenization, stop word removal, and TF-IDF vectorization, and training models such as Logistic Regression, Naïve Bayes, Random Forest, and deep learning models including LSTM and BERT. Experimental results demonstrate that ensemble models and transformer-based approaches outperform traditional classifiers, achieving high accuracy and robust detection capabilities. This study highlights the potential of machine learning in combating misinformation and provides a framework for developing scalable fake news detection systems.