Warning
This application is currently in alpha state and under active development. Please be aware that the API and features may change at any time.
Writing news articles about trending events requires journalists to sift through large amounts of content and deciding what to cover. This process can be time-consuming and mentally exhausting, with constant switching between different sources and ideas.
PulseSpotter is designed to assist journalists in the task of identifying newsworthy topics that are likely to become popular. By gathering information from various news sources and analyzing patterns over time, the system suggests emering trends, saving journalists time on research and deciding which stories to focus on.
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You can find usage examples with docker here: Docker example.
We welcome any contributions from the community! If you're interested in improving PulseSpotter, here are some ways you can get involved:
- Model improvements: Contribute by refining the scraping algorithms, improving topic modeling, or increasing the accuracy of predictions.
- Reporting Bugs: Identify issues or bugs in the system and suggest fixes.
- Feature Requests: Propose new features or improvements that could make PulseSpotter even more useful for journalists.
- Improving documentation: Assist in writing or refining documentation to make PulseSpotter more accessible to new users and contributors.
- Testing and feedback: Test the system, explore new features, and offer feedback to ensure a smooth user experience.
This project is licensed under the MIT License - see the LICENSE.md file for details.
If you want to get in touch feel free to reach out at pulsespottermedialab@gmail.com.
This project was supported by:
