Clickbait Detection with ANN, RNN, and LSTM

This project explores different deep learning models (ANN, RNN, and LSTM) for detecting clickbait headlines.

📂 Dataset

The dataset used comes from Kaggle:
Clickbait Dataset
It contains news headlines labeled as:

• 1 → Clickbait
• 0 → Non-clickbait

⚙️ Models

I implemented and trained three models using Keras (TensorFlow):

• Artificial Neural Network (ANN)
• Recurrent Neural Network (RNN)
• Long Short-Term Memory Network (LSTM)

📊 Results

The models were trained and tested on the dataset. Final accuracies:

• ANN: 0.9598
• RNN: 0.9267
• LSTM: 0.9475

🚀 How to Run

1. Clone this repository:

   git clone https://github.com/istec-iuc/clickbait-detector.git
   cd clickbait-detector

2. Install dependencies:

   pip install -r requirements.txt

3. Run the notebook to train the models

📌 Notes

• ANN, RNN, and LSTM were compared on the same dataset to evaluate their performance.
• Preprocessing included text cleaning, tokenization, and padding before feeding data into the models.