metadata

title: Fake News Detector
emoji: 📚
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
license: mit
short_description: 'Detects Fake News using the ensemble of 3 Models '

📚 Fake News Detector

Detects Fake News using an ensemble of 3 Models (Naive Bayes, Logistic Regression, and GloVe-based embeddings)

🚨 Important Disclaimer

⚠️ This project is built purely for educational and experimental purposes to explore basic Natural Language Processing (NLP) and Machine Learning (ML) techniques.

❗ It is not suitable for real-world fact-checking or decision-making.

The models used are simple, non-contextual, and cannot understand language nuances or factual correctness. Misusing this tool for serious analysis may lead to incorrect or harmful conclusions.

Please do not trust or rely on the outputs of this demo. It is meant for learning only.

🎯 Purpose

This project was created as a part of our research internship as a way to:

Practice building an ensemble model using different NLP approaches
Learn to deploy ML apps with Gradio and Hugging Face Spaces
Experiment with basic text classification on news headlines/articles

It is not a robust or reliable system for determining truth or accuracy in media.

⚙️ How It Works

This Fake News Detector uses an ensemble of 3 models:

Naive Bayes with TF-IDF – assigns 55% weight
Logistic Regression – assigns 10% weight
GloVe Embedding-Based Classifier – assigns 35% weight

Each model contributes a score between 0 and 1 indicating the likelihood of the input text being "Real." The final prediction is based on a weighted average.

📄 License & Attribution

This project is licensed under the MIT License.

Libraries and Tools Used:

🧠 GloVe Embeddings by Stanford NLP
🌐 Gradio Interface Library
📚 scikit-learn for model implementation
🛠 NLTK for basic NLP preprocessing
Dataset

📦 Installation

pip install -r requirements.txt
python app.py