The dataset used can be downloaded from this link. https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
Fake news is a problem that is spreading around the world, causing misinformation, panic, and distress among the general public. The increasing sophistication of fake news articles published in popular online mediums such as Twitter and Online News Media sites makes it an urgent and critical issue to address. This project is concerned with the development of machine learning and deep learning (LSTM) models for detecting fake news articles. The project evaluates and compares different text representation methods and their efficacy in a binary classification problem (fake news detection), as well as different machine learning and deep learning models.
The best performing classifier is then subjected to more rigorous tests in order to understand the robustness of the model from the dual perspectives of class imbalance in the training set and the problem of mislabeled training instances. The project follows the CRISP-DM methodology and has also hosted a live demo version of the various detectors developed online