🚀 Live Demo

Try it now 🔗: https://quickfactchecker.onrender.com/

📌 Project Overview

QuickFactChecker is a 🧠 machine learning–based web app that helps detect whether a 📰 news article is real or fake. It uses different models (e.g., Naive Bayes, LSTM 🧠) trained on the LIAR dataset 📚 to evaluate credibility and assist users in identifying potentially misleading information.

🌍 Vision

Build a more informed internet by making fact‑checking fast, accessible, and trustworthy for everyone.

🎯 Mission

• ⚡ Speed: deliver quick, reliable fake‑vs‑real assessments
• 🔎 Clarity: present results with confidence and clear cues
• 🧠 Learning: support multiple models and continuous improvement
• 🌐 Access: keep the app simple to use across devices

🤔 Why QuickFactChecker?

• 🧭 One‑place check: paste text or URL and get a verdict fast
• 🧮 Multiple models: Naive Bayes, Logistic Regression, Random Forest, LSTM
• 📊 Transparent output: result + confidence to judge reliability

🧭 Project Flowchart

flowchart TD
   A[User input: Text or URL] --> B[Fetch & preprocess]
   B --> C[Language/i18n setup]
   C --> D[Tokenize, normalize, clean]
   D --> E[Feature extraction: TF-IDF / Embeddings]
   E --> F{Model selected}
   F --> F1[Naive Bayes]
   F --> F2[Logistic Regression]
   F --> F3[Random Forest]
   F --> F4[LSTM]
   F1 --> G[Prediction: Real / Fake]
   F2 --> G
   F3 --> G
   F4 --> G
   G --> H[Confidence / metrics]
   H --> I[Display result in UI]

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🌟GSSoC

🌟 Exciting News...

🚀 This project is now an official part of GirlScript Summer of Code – GSSoC'25! 💻 We're thrilled to welcome contributors from all over India and beyond to collaborate, build, and grow QuickFactChecker! Let’s make learning and career development smarter – together! 🌟

👩‍💻 GSSoC is one of India’s largest 3-month-long open-source programs that encourages developers of all levels to contribute to real-world projects 🌍 while learning, collaborating, and growing together. 🌱

🌈 With mentorship, community support, and collaborative coding, it's the perfect platform for developers to:

• ✨ Improve their skills
• 🤝 Contribute to impactful projects
• 🏆 Get recognized for their work
• 📜 Receive certificates and swag!

🎉 I can’t wait to welcome new contributors from GSSoC 2025 to this QuickFactChecker project family! Let's build, learn, and grow together — one commit at a time. 🔥

✨ Features

• ✅ Fake news classification using ML models (Naive Bayes, Logistic Regression, Random Forest, and LSTM).
• ✅ Interactive web app built with Flask and HTML templates 🧪🖥️.
• ✅ Automated NLTK Setup to prevent missing resource errors 🧩.
• ✅ Preprocessed dataset included (train.tsv, test.tsv, valid.tsv) 🗂️.
• ✅ Notebooks for data analysis & experimentation (liar-data-analysis.ipynb, dataset.ipynb) 📓📈.
• ✅ Easy setup with requirements.txt ⚙️.

📂 Project Structure

QuickFactChecker/
├── .github/                              # GitHub-related configurations
│   ├── ISSUE_TEMPLATE/                   # Templates for creating GitHub issues
│   ├── workflows/                        # GitHub Actions workflows (CI/CD automation)
│   └── pull_request_template.md          # Template for new pull requests
│
├── .venv/                                # Virtual environment for Python dependencies
│   └── Lib/site-packages/                # Installed Python packages
│
├── Public/                               # Public assets (frontend files)
│   ├── css/                              # Stylesheets
│   ├── js/                               # JavaScript files
│   ├── locales/                          # Language translation files (i18n support)
│   ├── index.html                        # Main HTML file
│   ├── index_i18n.html                   # Multilingual HTML file
│   ├── script.js                         # Main frontend script
│   └── style.css                         # Main stylesheet
│
├── app.py                                # Flask application entry point
├── install_i18n.py                       # Script to set up internationalization (i18n)
│
├── module/                               # Custom Python modules
│
├── dataset/                              # Datasets and analysis notebooks
│   ├── liar/                             # LIAR dataset folder
│   ├── dataset.ipynb                     # General dataset exploration notebook
│   ├── fake-news-detection-ml-comparison.ipynb  # Comparison of ML models
│   ├── fake-news-detection-using-lr.ipynb       # Logistic Regression implementation
│   ├── fake-news-detection-using-lstm.ipynb     # LSTM model notebook
│   ├── fake-news-detection-using-nb.ipynb       # Naive Bayes model notebook
│   ├── fake-news-detection-using-svm.ipynb      # SVM model notebook
│   ├── fake-news-detection-using-xgboost.ipynb  # XGBoost model notebook
│   └── liar-data-analysis.ipynb                 # Data analysis for LIAR dataset
│
├── results/                             # Folder for storing results, graphs, and metrics
│
├── scripts/                             # Additional scripts used in the project
│
├── tests/                               # Unit and integration tests
│   ├── test_app.py                      # Tests for main app
│   ├── tests_app.py                     # Additional test scripts
│   └── tests_dummy.py                   # Dummy test file
│
├── utils/                               # Utility modules
│   └── fetch_url.py                     # Helper function to fetch and preprocess URLs
│
├── CODE_OF_CONDUCT.md                   # Community guidelines
├── CONTRIBUTING.md                      # Contribution guidelines
├── LICENSE                              # License information
├── LOGO.svg                             # Project logo
├── GSSoC.png                            # GSSoC banner image
├── MULTILINGUAL_SUPPORT.md              # Guide for adding multiple language support
├── Readme.md                            # Main project documentation
├── debug.log                            # Debugging log file
├── .env                                 # Environment variables
├── .gitignore                           # Files to be ignored by Git
├── .gitattributes                       # Git configuration for line endings
├── .coverage                            # Code coverage report
└── files.txt                            # Miscellaneous file list

⚙️ Installation & Setup

1. 📥 Clone the repository and navigate into it:

   git clone https://github.com/Deepika14145/QuickFactChecker.git
   cd QuickFactChecker

2. 🧪 Create virtual environment (optional but recommended)

      python -m venv venv

3. ▶️ Activate the virtual environment:

      source venv/bin/activate   # for Linux/Mac
      venv\Scripts\activate      # for Windows

4. 📦 Install the required dependencies:

   pip install -r requirements.txt

5. 📚 Download NLTK Corpora:

python scripts/setup_nltk.py

�️ Troubleshooting

• ⛔ NLTK resource errors: run python scripts/setup_nltk.py again; check internet connection
• 📦 Import errors: ensure virtualenv is active and pip install -r requirements.txt ran without errors
• 🌐 CORS or fetch failures for URLs: verify the target site is reachable; try plain text input
• 🧪 Notebook issues: update Jupyter and restart kernel; ensure correct interpreter (venv)
• 🔌 Port in use: stop prior app instance or use a different port

📊 Baseline Model Comparison

We evaluated three models on the LIAR dataset using TF-IDF features. Example results 📈 (accuracy & precision): example:

Model	Accuracy	Precision
Naive Bayes	0.XXXX	0.XXXX
Logistic Regression	0.XXXX	0.XXXX
Random Forest	0.XXXX	0.XXXX

Logistic Regression achieved the highest accuracy among the tested baselines.

🔧 Run the comparison script

To reproduce these results, run:

scripts/fake_news_logreg_rf.py

▶️ Usage

1. 🟢 Run the following command to start the application:

   python app.py

2. 📰 The app will provide predictions on whether a news article is real or fake based on the input.

🛠️ Model Training

To retrain or experiment with the models, run the provided Jupyter notebooks. Ensure your virtual environment is activated and all dependencies are installed.

🧮 Naive Bayes

Run the notebook:

jupyter notebook fake-news-detection-using-nb.ipynb

🧠 LSTM

Run the notebook:

jupyter notebook fake-news-detection-using-lstm.ipynb

📊 Dataset Analysis

jupyter notebook liar-data-analysis.ipynb

🤝 Contributing

Contributions are welcome! Whether you’re fixing typos, improving docs, or adding new features — every PR helps. Follow these steps:

1. 🍴 Fork the repository
2. 🌿 Create a new branch (git checkout -b feature-name)
3. 🛠️ Make your changes
4. 💬 Commit your changes (git commit -m 'description of your feature/fix')
5. ⬆️ Push to the branch (git push origin feature-name)
6. 🔁 Create a Pull Request

Please read CONTRIBUTING.md and follow our Code of Conduct.

📦 Deployment

The application is deployed on Render ☁️ and accessible at: https://quickfactchecker.onrender.com/

Deployment Features:

• ✅ Free hosting on Render 💸
• ✅ Auto-deployment from GitHub commits 🔄
• ✅ Production-ready with Gunicorn server 🚀
• ✅ HTTPS enabled by default 🔒
• ✅ Optimized requirements for faster build times ⚡

Technical Stack:

• Backend: Flask (Python) 🐍
• Server: Gunicorn 🛠️
• Platform: Render ☁️
• CI/CD: GitHub integration 🔗

📧 Contact

For queries, feedback, or guidance regarding this project, you can contact the mentor assigned to the issue:

• 📩 GitHub (Owner): Deepika14145
• 💬 By commit/PR comments: Please tag the mentor in your commit or pull request discussion for direct feedback.

Original Repository: QuickFactChecker

Contributor

A heartfelt thank you to all the contributors who have dedicated their time and effort to make this project a success.
Your contributions—whether it’s code, design, testing, or documentation—are truly appreciated! 🚀

Thanks to all the wonderful contributors 💖

See full list of contribution from contributor Contributor Graph

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

If you find this project useful, please give it a ⭐️! Your support is appreciated!

Feel free to contribute or suggest new features!🙏