Authors: Adlin Jebakumari, Uzefa Begum, Kathi Harshitha Reddy, Mohammed Rameez
Abstract: The growth of digital media in recent years has created a major public issue. This is evident in the increase of false information, often called fake news. Fake news refers to any news item that contains false information for the audience. This research project combines traditional machine learning methods with modern deep learning techniques to detect fake news using a hybrid detection system. The news articles will undergo several preprocessing steps: text cleaning, tokenization, stop word removal, and text data normalization for analysis. The team will preprocess the textual data, which will then be converted into numeric data for machine learning and deep learning models. This will use feature extraction methods like tokenization and word embeddings. The project will apply traditional machine learning models to create training data that captures the unique features of fake news and real news articles. The study will also use various deep learning models, including LSTM Networks and BERT. These models will help identify sequential and contextual relationships in articles by understanding complex language patterns and the connections among different types of text data.
DOI: https://doi.org/10.5281/zenodo.19508418
