Automated Fake Content Detection Using TF-IDF-Based Machine Learning and LSTM-Driven Deep Learning Models

The rapid spread of misinformation across online platforms has made automated fake news detection essential. This project develops and compares machine learning (SVM, Decision Tree) and deep learning (LSTM) models to classify news headlines from the GossipCop and PolitiFact datasets as real or fake. After extensive preprocessing— including text cleaning, lemmatization, TF-IDF vectorization, and sequence tokenization—the models are trained and evaluated using standard performance metrics. Results show that SVM provides a strong baseline, but the LSTM model achieves higher accuracy and F1-scores by capturing deeper semantic and contextual patterns in the text. The study highlights the challenges of domain variation and subtle linguistic cues, while demonstrating that context-aware deep learning methods offer superior capability for automated fake content detection.