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---
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:
1. **Naive Bayes with TF-IDF** – assigns 55% weight
2. **Logistic Regression** – assigns 10% weight
3. **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](https://nlp.stanford.edu/projects/glove/)
- 🌐 [Gradio Interface Library](https://www.gradio.app/)
- 📚 [scikit-learn](https://scikit-learn.org/) for model implementation
- 🛠 [NLTK](https://www.nltk.org/) for basic NLP preprocessing
- [Dataset](https://www.kaggle.com/datasets/stevenpeutz/misinformation-fake-news-text-dataset-79k)
## 📦 Installation
```bash
pip install -r requirements.txt
python app.py
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