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https://github.com/monzerdev/fake-news-detection

Project implementing machine learning models to detect fake news articles. Utilizes Deep Neural Networks, Support Vector Machines (SVM), and Ensemble methods (Random Forest). Developed using Python with scikit-learn, PyTorch, and nltk.
https://github.com/monzerdev/fake-news-detection

dnn fakenewsdetection machinelearning nlp nltk python pytorch random-forest scikit-learn svm

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Project implementing machine learning models to detect fake news articles. Utilizes Deep Neural Networks, Support Vector Machines (SVM), and Ensemble methods (Random Forest). Developed using Python with scikit-learn, PyTorch, and nltk.

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README 

        
Fake News Detection



This project involves developing models to detect fake news articles using various machine learning techniques. The models used include Deep Neural Networks, Support Vector Machines (SVM), and Ensemble methods (Random Forest). The project is implemented using Python and several libraries including scikit-learn, PyTorch, and nltk.



Project Structure:   



- `Fake_News_Detection_DNN_SVM_Ensemble.ipynb`: The main Jupyter Notebook containing all the code for data preprocessing, model training, evaluation, and testing.

- `Datasets`: This directory is supposed to contain the datasets used in this project. The datasets are too large to be stored on GitHub, so they are hosted on Google Drive.



Datasets:



Due to their large size, the datasets are hosted on Google Drive. Please download them from the link below:



https://drive.google.com/file/d/1kClWEaFzwgBxRrgJP6-JYkR8Sv9V30nE/view?usp=sharing



Once downloaded, place the extracted CSV files (`stemmed_dataset1_40k.csv` and `stemmed_WELFake_70k.csv`) in the `Datasets/` directory within the project folder.



Project Workflow:



1. Data Preprocessing

   - The dataset is preprocessed to remove noise and irrelevant information. The preprocessing steps include:

     - Removing URLs

     - Converting text to lowercase

     - Removing stopwords

     - Stemming words



2. Model Training

   - Three different models were trained on the preprocessed data:

     - Deep Neural Network (DNN): A multi-layer perceptron with dropout regularization.

     - Support Vector Machine (SVM): A linear SVM model.

     - Ensemble Method (Random Forest): A random forest ensemble model with bootstrapped sampling.



3. Results

   - For detailed results, including accuracy, precision, recall, and F1-score metrics for the various models used in this project, as well as additional information, please refer to the full paper available via the Google Drive link below:



https://drive.google.com/file/d/1uMsjUeO1GI4E-n8p22hN6CHvjil2QTLm/view?usp=sharing



To run this project, follow these steps:



1. Clone the repository:

   git clone https://github.com/MonzerDev/Fake-News-Detection.git



2. Navigate to the project directory:

   cd Fake-News-Detection



3. Install the required Python packages:

   pip install -r requirements.txt

   Note: You need to manually create a `requirements.txt` file if it's not already included. This file should list all the necessary Python packages.



4. Download the datasets from the link provided above and extract them into the `Datasets/` directory.



5. Run the Jupyter Notebook to execute the code and reproduce the results:

   jupyter notebook Fake_News_Detection_DNN_SVM_Ensemble.ipynb



Contributing:



Contributions are welcome! If you have any ideas, suggestions, or improvements, feel free to open an issue or submit a pull request.



Contact:



For any questions or suggestions, please feel free to contact me at [[email protected]].