📰 Truth or Trap — Fake News Detection with ML & DL

Truth or Trap is a research-focused project exploring fake news detection using machine learning (ML) and deep learning (DL) techniques. Written in Springer review format, this project evaluates multiple supervised models on benchmark datasets to determine which models best distinguish truth from misinformation.

💡 Smarter models fall for less clickbait! 🤖📰

🔍 About the Project

Fake news spreads rapidly online, influencing opinions and even elections. This project investigates ML & DL models for fake news detection, comparing classical classifiers with advanced deep learning approaches using NLP techniques.

Key goals:

• Evaluate the performance of ML & DL models for fake news detection
• Explore text preprocessing, feature engineering, and context-aware architectures
• Provide a research-ready, reproducible framework for experimentation

🧠 Techniques & Models

Traditional Machine Learning

• Logistic Regression
• Naive Bayes
• Support Vector Machines (SVM)
• Random Forest

Deep Learning & NLP

• LSTM / Bi-LSTM
• Word embeddings (TF-IDF, Word2Vec)
• Context-aware architectures

Text Preprocessing & Feature Engineering

• Tokenization & vectorization
• Stopwords removal & normalization
• Sequence padding and embedding

📂 Project Structure

TruthOrTrap/
│
├── paper/                 # Springer-style research paper
├── notebooks/             # Jupyter notebooks with experiments
├── src/                   # Model implementations
├── datasets/              # Sample datasets / links
├── results/               # Evaluation metrics and visualizations
├── requirements.txt       # Python dependencies
└── README.md