abdelazizabakartahir/senegal-whatsapp-disinformation-detector

# 🇸🇳 Senegal WhatsApp Disinformation Detector ## 📌 Overview This project detects viral political disinformation on WhatsApp in Senegal using Machine Learning. It helps fact-checking teams prioritize dangerous messages during election periods. **Live Demo:** [Link to your Streamlit app] **Author:** Abdelaziz Abakar Tahir - AISIP Cohort 1 | Africa AI Hub --- ## 🎯 Problem Statement During election periods in Senegal, deepfakes and AI-generated disinformation spread rapidly on WhatsApp. Citizens cannot distinguish real content from fake. No accessible detection tool exists in French or Wolof. **Key statistics from my research:** - 80% of survey respondents were exposed to political deepfakes - 73% consider the problem "very serious" - 90% would use a detection tool if available --- ## 🚀 Solution A Machine Learning classifier that predicts whether a political WhatsApp message has high viral potential. | Feature | Description | |---------|-------------| | Input | Political WhatsApp message (text) | | Output | Viral probability score (0-100%) | | Model | Random Forest (F1 Score: 0.86) | | Deployment | Streamlit web application | --- ## 📊 Dataset - **Size:** 2,000 synthetic messages - **Label distribution:** 30% viral, 70% normal - **Features:** message length, word count, capitals ratio, emoji count, political keywords, call-to-action, TF-IDF (50 features) --- ## 🤖 Models Trained | Model | F1 Score | |-------|----------| | Logistic Regression | 0.80 | | Random Forest | **0.86** 🏆 | | Gradient Boosting | 0.85 | | Neural Network (Keras) | 0.84 | | Ensemble Voting | 0.86 | --- ## 🛠️ Technology Stack | Component | Technology | |-----------|------------| | Data Processing | Python, Pandas, NumPy | | Visualization | Matplotlib, Seaborn | | ML Models | Scikit-learn | | Neural Networks | TensorFlow / Keras | | Deployment | Streamlit | | Version Control | Git / GitHub | | Environment | Google Col …