Welcome to Informfully (GitHub & Website)! Informfully is an open-source research tool for content distribution and running user experiments. It allows you to push algorithmically curated text, image, audio, and video content to users and automatically generates a detailed log of their consumption history. The benefit of Informfully, compared to other frameworks, is that it offers a complete end-to-end solution with all necessary components along the entire recommender pipeline.

Links and Resources: Repositories | Website | X | Documentation | DDIS@UZH | Google Play | App Store

Informfully is a domain-agnostic and platform-independent solution to fit your specific needs. It is designed to accommodate different experiment types through versatility, ease of use, and scalability. The core components are:

• Platform Repository: Get full access to the Informfully platform (app and web).
• Scrapers Repository: Use our content scrapers to get your hands on news articles.
• Datasets Repository: See a sample export of all the information Informfully gives you.
• Experiments Repository: Shared recommendation workflows to reproduce all our findings.
• Recommenders Repository: Showcasing all our diversity-optimized recommender algorithms.
• Documentation Repository: Overview and guides for deploying your own instance of Informfully.

Note: Our GitHub repositories allow you to run your own instance of Informfully. If you would like to use Informfully as a cloud service hosted at the University of Zurich, please contact us. Free demo accounts are available upon request: info@informfully.ch

User Studies and Experiments powered by Informfully:

• Nudges for News Recommenders: Prominent Article Positioning Increases Selection, Engagement, and Recall of Environmental News, but Reducing Complexity Does Not
• IDEA – Informfully Dataset with Enhanced Attributes
• Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys Challenge
• Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study
• Benefits of Diverse News Recommendations for Democracy: A User Study

Papers on the Informfully Research Infrastructure:

• Informfully Recommenders – Reproducibility Framework for Diversity-aware Intra-session Recommendations
• D-RDW: Diversity-Driven Random Walks for News Recommender Systems
• Informfully – Research Platform for Reproducible User Studies

Work on Visual Generative AI for News:

• NewsImages in MediaEval 2025 – Comparing Image Retrieval and Generation for News Articles
• An Empirical Exploration of Perceived Similarity between News Article Texts and Images
• Prompt-based Alignment of Headlines and Images Using OpenCLIP

Position Papers on Normativity and Diversity in News:

• Classification of Normative Recommender Systems
• Spotlight on Artificial Intelligence and Freedom of Expression: A Policy Manual
• Diversity in News Recommendation

You are welcome to contribute to the Informfully ecosystem and become a part of our community. Feel free to:

• Fork any of the Informfully repositories.
• Suggest new features in Future Release.
• Make changes and create pull requests.

Please post your feature requests and bug reports in our GitHub issues section.