metadata
license: mit
🔍 VeriLang — Vernacular Misinformation Detector
AI-powered fake news detector for WhatsApp forwards in Hindi, Gujarati, Marathi and Telugu using Google's MuRIL model.
🎯 Problem Statement
WhatsApp misinformation in Indian regional languages is a serious problem affecting millions of people daily. Existing fake news detectors work only for English. VeriLang solves this by detecting misinformation in 4 major Indian languages — Hindi, Gujarati, Marathi and Telugu.
✨ What Makes This Unique
| Feature | Other Projects | VeriLang |
|---|---|---|
| Languages | English only | 4 Indian languages |
| Model | Generic BERT | MuRIL — Indian language specialist |
| Target | General news | WhatsApp forwards specifically |
| Explainability | None | SHAP word importance |
| Deployment | Jupyter only | Live web app |
📊 Dataset
- Source: Extracted 4 different language dataset from Zenodo — Multilingual Fake News Detection (https://zenodo.org/records/11408513)
- Size: 49,426 articles
- Labels: Fake (0) / Real (1)
- Languages: Hindi, Gujarati, Marathi, Telugu
| Language | Total | Fake | Real |
|---|---|---|---|
| Hindi | 15,051 | 7,599 | 7,452 |
| Gujarati | 14,830 | 6,145 | 8,685 |
| Telugu | 11,424 | 4,795 | 6,629 |
| Marathi | 8,121 | 707 | 7,414 |
🔬 Methodology
Phase 1 — Data Cleaning
- Removed URLs, HTML tags, extra spaces
- Kept unicode scripts intact for Indian languages
- No duplicates, no missing values
Phase 2 — Baseline ML Models
- TF-IDF with character n-grams (2-4 grams)
- Character n-grams work better than word n-grams for Indian scripts
- Trained Logistic Regression, SVM, Naive Bayes
Phase 3 — MuRIL Fine-tuning
- Used google/muril-base-cased from HuggingFace
- MuRIL is trained on 17 Indian languages
- Fine-tuned for 3 epochs on Google Colab T4 GPU
- Applied class weights to fix Marathi imbalance
📈 Model Performance
Overall Accuracy
| Model | Accuracy |
|---|---|
| Logistic Regression | 99.63% |
| Naive Bayes | 97.77% |
| SVM | 99.84% |
| MuRIL (final) | 99.92% |
Per Language Accuracy
| Language | SVM | MuRIL |
|---|---|---|
| Hindi | 99.66% | 99.80% |
| Gujarati | 99.86% | 99.93% |
| Marathi | 99.94% | 100.00% |
| Telugu | 99.96% | 100.00% |
Overfitting Check
| Model | Train | Test | Gap | Status |
|---|---|---|---|---|
| Logistic Reg | 99.60% | 99.63% | 0.03% | No overfit |
| SVM | 99.99% | 99.84% | 0.15% | No overfit |
| MuRIL | — | 99.92% | — | CV std 0.05% |
🛠 Tech Stack
| Category | Tools |
|---|---|
| Deep Learning | PyTorch, HuggingFace Transformers |
| Model | google/muril-base-cased |
| ML Baseline | Scikit-learn, TF-IDF |
| Explainability | SHAP |
| Web App | Streamlit |
| Model Hosting | HuggingFace Hub |
| Training | Google Colab T4 GPU |
💻 How to Run Locally
git clone https://github.com/Maitry09/verilang-misinformation-detector
cd verilang-misinformation-detector
pip install -r requirements.txt
streamlit run app.py
Add .streamlit/secrets.toml:
HF_TOKEN = "your_token_here"
⚖️ Ethical Considerations
- Tool is for awareness only — not a final judgment system
- Always verify from official sources like PIB, ANI, PTI
- Model may have bias toward certain writing styles
- Marathi fake detection improved with augmentation but still has fewer training samples
🚀 Future Work
- Add Bengali, Tamil, Kannada support
- Work on mixture of language
- Build WhatsApp bot integration using Twilio API
- Add image-based misinformation detection
- Real-time news verification via fact-check APIs
- Mobile app for direct WhatsApp forward checking
👤 Author
Maitry
- GitHub: @Maitry09
- Live App: [verilang.streamlit.app]
📄 License
MIT License
⭐ Star this repo if you found it useful!