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Project RootSource

Welcome to RootSource, a modular framework designed to analyze complex events, uncover patterns, and identify origins and potential misinformation. This open-source framework is ideal for researchers, analysts, and developers aiming to explore the layers behind events and narratives.

Features

  1. Layered Framework:
    • Truth Layer: Analyze perspectives, biases, and narratives.
    • Origin Layer: Trace historical contexts and unintended consequences.
    • Complexity Layer: Map interdependencies and feedback loops.
  2. API Integration:
    • Fetch live data from APIs like NewsAPI, Twitter/X, and Reddit.
    • Perform sentiment and dependency analysis using NLP APIs.
  3. Modular Design:
    • Extendable to include additional layers or data sources.
    • Adaptable for different scenarios, from political analysis to marketing trends.

Setup Instructions

1. Clone the Repository

git clone https://github.com/your_username/RootSource.git
cd RootSource

2. Install Dependencies

Install required Python libraries:

pip install requests

3. Configure API Access

Copy the config_template.json file, rename it to config.json, and fill in your API credentials:

{
    "news_api_key": "your_newsapi_key_here",
    "twitter_api_key": "your_twitter_api_key_here",
    "twitter_api_secret": "your_twitter_api_secret_here",
    "reddit_api_key": "your_reddit_api_key_here",
    "google_nlp_key": "your_google_nlp_key_here",
    "openstreetmap_url": "https://api.openstreetmap.org"
}

4. Run Examples

Start exploring the framework by running the example script:

python example_/main_example.py

The example script demonstrates:

  1. How to load API credentials from config.json.
  2. Fetching narrative data (static or via APIs, if configured).
  3. Analyzing narratives for perspectives and biases using the Truth Layer.

Layers Overview

Truth Layer

  • Purpose: Analyze perspectives and biases in event narratives.
  • Features:
    • Accept multiple narrative inputs (e.g., government, media, public opinion).
    • Identify and categorize biases.
    • Output a structured overview of competing perspectives.
  • Example: 
    from code.truth_layer import analyze_truth
narratives = {
    "Government": "The government narrative focuses on stability and order.",
    "Media": "Exclusive coverage reveals potential government agendas.",
    "Public": "The public narrative highlights concerns about freedom and transparency."
}
result = analyze_truth(narratives)
print(result)

Origin Layer

  • Purpose: Trace historical contexts and unintended consequences.
  • Features:
    • Accept historical data inputs.
    • Perform root cause analysis.
    • Highlight unintended outcomes from key actions or decisions.
  • Example: Coming soon!

Complexity Layer

  • Purpose: Map interdependencies and feedback loops.
  • Features:
    • Analyze interactions between entities.
    • Visualize dependencies and feedback loops.
    • Provide insights into system dynamics.
  • Example: Coming soon!

Folder Structure

RootSource/
|— README.md
|— config_template.json  # Template for API credentials (user must rename to config.json)
|— code/
|    |— truth_layer.py
|    |— origin_layer.py
|    |— complexity_layer.py
|— example_/
|    |— main_example.py  # Demonstrates integration of all layers with API placeholders

What's Next?

  1. Extend the Framework:
    • Add more customizable layers and sublayers.
    • Enhance flexibility for domain-specific analyses.
  2. Enable Advanced Applications:
    • Migrate the framework for real-time and large-scale scenarios.
  3. Possibility:
    • Add visualization tools for mapping dependencies and feedback loops.

Contributing

Contributions are welcome! To contribute:

  1. Fork the repository.
  2. Create a new branch for your feature/bugfix.
  3. Submit a pull request with a detailed explanation.

License

This is licensed under the MIT License. See the LICENSE file for more information.

Contact

If you have any questions or suggestions, feel free to reach out via GitHub or x.com/notthepeanutbar