A Modular Framework for Mitigating and Predicting Fake News Using Machine Learning

In today’s digital age, fake news has become a major challenge, shaping public perception and influencing decision-making. To tackle this issue, our project introduces a machine learning-based system designed to detect fake news with high accuracy. Using natural language processing (NLP) techniques, the system effectively analyzes news articles to identify misleading information. It begins by preprocessing text with the Porter Stemmer algorithm, which simplifies words to their root forms. This step ensures consistency and improves the model’s performance. To understand the significance of words in a news article, we use Term Frequency-Inverse Document Frequency (TF-IDF) and filter out common but unimportant words with Stopwords representations. For classification, we rely on Logistic Regression, a widely used algorithm known for its reliability in distinguishing between two categories—real and fake news. Its balance of accuracy and interpretability makes it an ideal choice for this task. The model is trained and tested on a labeled dataset, showcasing its effectiveness in detecting misinformation. By providing an automated and trustworthy tool for fake news detection, this system contributes to the fight against misinformation in digital media, promoting a more informed and aware society.