{"id":15157753,"url":"https://github.com/monzerdev/fake-news-detection","last_synced_at":"2026-01-21T01:02:01.430Z","repository":{"id":277380411,"uuid":"926122339","full_name":"MonzerDev/Fake-News-Detection","owner":"MonzerDev","description":"A robust fake news detection system leveraging machine learning models (SVM and Random Forest) to identify political misinformation. 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The system uses advanced machine learning models, including Support Vector Machines (SVM) and Random Forest, with optimized preprocessing techniques to ensure high accuracy and reliability.\n\n---\n\n## Project Structure\n\n- **SVM_RF_CountVec_Dataset1.ipynb**: Implements SVM and Random Forest classifiers using CountVectorizer for Dataset1.\n- **SVM_RF_CountVec_WELFAKE.ipynb**: Implements SVM and Random Forest classifiers using CountVectorizer for the WELFAKE dataset.\n- **SVM_RF_TFIDF_Dataset1.ipynb**: Implements SVM and Random Forest classifiers using TF-IDF for Dataset1.\n- **SVM_RF_TFIDF_WELFAKE.ipynb**: Implements SVM and Random Forest classifiers using TF-IDF for the WELFAKE dataset.\n\n---\n\n## How to Run the Project\n\n### 1. Install Dependencies\n\nEnsure Python is installed on your system. Install the required Python packages using:\n\n\n```bash\npip install numpy pandas scikit-learn matplotlib\n```\n\n### 2. Download Datasets\n\nThe datasets used in this project are large and are hosted on Google Drive. Download them using the following link:\n\n[Download Datasets](https://drive.google.com/file/d/127zwpZPnoWBx17vOKPgRC5rUcT1J01KQ/view?usp=sharing)\n\nEnsure the datasets are placed in the appropriate directory before running the scripts.\n\n### 3. Running the Models\n\nRun the appropriate Jupyter notebooks to train and test the models:\n\n- For SVM and Random Forest with CountVectorizer:\n  - Dataset1: `SVM_RF_CountVec_Dataset1.ipynb`\n  - WELFAKE: `SVM_RF_CountVec_WELFAKE.ipynb`\n\n- For SVM and Random Forest with TF-IDF:\n  - Dataset1: `SVM_RF_TFIDF_Dataset1.ipynb`\n  - WELFAKE: `SVM_RF_TFIDF_WELFAKE.ipynb`\n\n### 4. Training the Models (Optional)\n\nTo retrain the models from scratch, execute the respective notebooks and ensure the datasets are correctly preprocessed.\n\n---\n\n## How It Works\n\n1. **Data Preprocessing:**\n   - Large datasets are preprocessed using techniques such as text normalization, tokenization, and vectorization (CountVectorizer and TF-IDF).\n\n2. **Model Training:**\n   - Machine learning classifiers (SVM and Random Forest) are trained on the preprocessed datasets to learn patterns and detect fake news.\n\n3. **Evaluation:**\n   - The models are evaluated on separate test datasets using metrics such as accuracy, precision, recall, and F1-score.\n\n4. **Results:**\n   - The trained models achieve high performance, with precision, recall, and F1-scores exceeding 98% in most cases.\n\n---\n\n## Requirements\n\n- Python 3.x\n- NumPy\n- Pandas\n- Scikit-learn\n- Matplotlib\n\n---\n\n## Notes\n\n- The project focuses on political misinformation but can be extended to other domains.\n- Ensure datasets are preprocessed and formatted correctly before training or testing the models.\n- Pre-trained models and results can be accessed in the provided notebooks.\n\n---\n\n## Contributing\n\nContributions are welcome! If you have suggestions or improvements, feel free to open an issue or submit a pull request.\n\n---\n\n## Contact\n\nFor any questions or suggestions, feel free to contact me at [monzerkoukou@gmail.com](mailto:monzerkoukou@gmail.com).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonzerdev%2Ffake-news-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmonzerdev%2Ffake-news-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonzerdev%2Ffake-news-detection/lists"}