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.