Introduction

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HonestyMeter is a framework for evaluating objectivity and bias in media content, including text, images, audio, and video. It uses large language models to analyze the media content and identify manipulative techniques that may be present. The framework is capable of detecting over 100 different manipulation techniques, such as sensationalism, framing, and selective reporting, among others.The framework provides users with an overall objectivity score, feedback on manipulative techniques, and suggestions for improvement.

To see it HonestyMeter in action, you can watch short video demo (7 seconds) or visit our website: HonestyMeter.com

Note: This is the very basic first experimental DEMO version, which currently only supports the analysis of text. Additional features and improvements will be added in near feature.

Installation and Configuration

1. Install required dependencies: npm install

2. Rename the .env_example file to .env: mv .env_example .env

3. Replace OPEN_AI_KEY with your OpenAI API key in the .env file.

4. Run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev

Open http://localhost:3000 with your browser to see the result.

Usage

1. Paste the article text that you would like to analyze and press the submit button.

2. Wait for the server to respond (may take 30-180 seconds) and see your report.

Contributing

If you would like to contribute to the development of the HonestyMeter framework, please feel free to join disscussions, visit our project page, or open an issue on the repository. We appreciate any feedback and contributions to help improve the framework and its usability.

License

HonestyMeter is released under the APACHE 2.0 License.

For more information on the HonestyMeter framework and how it can help increase transparency and objectivity in media content, please see "about" page in our website HonestyMeter.com/about

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