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Contributing to Social Street Smart

Thank you for your interest in contributing to Social Street Smart! This document provides detailed information on how to set up the project, run various components, and contribute effectively.

Table of Contents

  1. Project Structure

  2. Setting Up the Project

  3. Extracting Data and Models

  4. Running the Components

  5. Contributing Guidelines

  6. Code Style

  7. Reporting Issues

Project Structure

Social-Street-Smart/
├── client/                 # Frontend (Chrome extension)
├── server/                 # Backend services
│   ├── clickbait/
│   ├── hate-speech/

│   ├── fakenews/
│   ├── imageAPI/
│   └── news-origin/
├── ML/              # Machine learning models
│   ├── clickbait/
│   ├── hate-speech/
│   └── fakenews/

└── docker-compose.yml

Setting Up the Project

Prerequisites

  • Node.js (v14+)
  • Python (v3.8+)
  • Docker and Docker Compose

Frontend Setup

  1. Navigate to the client directory:

    cd client

  2. Install dependencies:

    npm install

  3. Build the extension:

    npm run build

  4. Load the extension in Chrome:

    • Open Chrome and go to chrome://extensions/
    • Enable "Developer mode"
    • Click "Load unpacked" and select the dist folder

Deployed Docker Images on Render

Backend Setup

The backend services are containerized using Docker:

  1. Navigate to the server directory:

    cd server

  2. Start all services:

    docker compose up

Extracting Data and Models

Some data and models are too large to be included in the GitHub repository. Follow these steps to obtain and set up the necessary files:

Fake News Model

  1. Download the Fake News datasets from Kaggle: AOSSIE Fake News Detection Datasets
  2. Extract and place the files in server/fakeNewsAPI/ML/ and server/fakeNews/predictions/ML/
  3. Download the GloVe word embeddings (glove.6B.100d.txt) from Stanford NLP and place it in both directories mentioned above

Click-Bait Model

  1. Download the Click-Bait dataset from Kaggle: AOSSIE Click-Bait Dataset
  2. Extract and place the files in the appropriate directory under ML/clickbait/

Google News Vectors

  1. Download the Google News vector dataset from Kaggle: Google News Vectors
  2. Extract and place the files in the appropriate directory under ML/

Toxic Comment/Hate Speech Models

  1. Download the GloVe word embeddings (glove.6B.zip) from Stanford NLP
  2. Extract and place the files in Toxic Comment/Data/glove.6B/
  3. Download the Toxic Comment Classification Challenge dataset from Kaggle: Jigsaw Toxic Comment Classification Challenge
  4. Extract and place the files in Toxic Comment/Data/toxic-comment/

Image API Credentials

  1. Follow the instructions in this video to generate Google App Credentials: Generate Google App Credentials
  2. Save the generated JSON file as GoogleAppCreds.json in server/imageAPI/tmp/

News Origin API Key

  1. Go to Google Custom Search API
  2. Click on "Get a Key" and follow the instructions to create a new project and generate an API key
  3. Create a .env file in server/News_Origin/ and add your API key: 
    GOOGLE_API_KEY=your_api_key_here

Running the Components

Frontend (Chrome Extension)

After building the extension, you can load it into Chrome as an unpacked extension. Any changes to the source code will require rebuilding the extension and reloading it in Chrome.

Backend Servers

The Docker Compose file will start all the backend services. You can access them at the following endpoints:

  • Clickbait API: http://localhost:5000/predict
  • Hate Speech API: http://localhost:5001/predict
  • Fake News API: http://localhost:5002/predict
  • Image Disinformation API: http://localhost:5003/analyze
  • News Origin API: http://localhost:5004/origin

Contributing Guidelines

  1. Fork the repository
  2. Create a new branch: git checkout -b feature-name
  3. Make your changes
  4. Run tests (if available)
  5. Commit your changes: git commit -m 'Add some feature'
  6. Push to the branch: git push origin feature-name
  7. Submit a pull request

Code Style

  • For Python: Follow PEP 8
  • For JavaScript/TypeScript: Use ESLint with the project's configuration

Reporting Issues

Use the GitHub Issues tab to report bugs or suggest enhancements. Please provide as much detail as possible, including:

  • A clear and descriptive title
  • Steps to reproduce the issue
  • Expected behavior
  • Actual behavior
  • Any relevant logs or screenshots

Thank you for contributing to Social Street Smart!