## What Is Informfully?
Do you want to push news, videos, songs, or pictures to user panels and see how they react? We got you covered! Informfully is an all-in-one content delivery platform that allows you to set up your users studies within minutes. No coding required! Run it on Android, iOS, or in your browser. Give it a try and
download it right now!
Informfully is fully open-source. Become a member today!
## Community
Thank you for choosing Informfully!
Access the code on GitHub and become part of our community. We’d love to hear your feedback and welcome any contributions. Don’t hesitate to reach out at [info@informfully.ch](mailto:info@informfully.ch).
## How does Informfully work?
## Free for Academia
As part of Informfully's committment to research, the services are offered for free to research institutions. Take a look at the projects below to see how Informfully was successfuly used in the past to support research.
## Media
## Citation
If you use any Informfully code/repository in a scientific publication,
we ask you to cite the following papers:
- [Informfully - Research Platform for Reproducible User
Studies](https://www.researchgate.net/publication/383261885_Informfully_-_Research_Platform_for_Reproducible_User_Studies),
Heitz *et al.*, Proceedings of the 18th ACM Conference on
Recommender Systems, 2024.
```tex
@inproceedings{heitz2024informfully,
title={Informfully - Research Platform for Reproducible User Studies},
author={Heitz, Lucien and Croci, Julian A and Sachdeva, Madhav and Bernstein, Abraham},
booktitle={Proceedings of the 18th ACM Conference on Recommender Systems},
year={2024}
}
```
- [Deliberative Diversity for News Recommendations -
Operationalization and Experimental User
Study](https://dl.acm.org/doi/10.1145/3604915.3608834), Heitz *et
al.*, Proceedings of the 17th ACM Conference on Recommender Systems,
813--819, 2023.
```tex
@inproceedings{heitz2023deliberative,
title={Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study},
author={Heitz, Lucien and Lischka, Juliane A and Abdullah, Rana and Laugwitz, Laura and Meyer, Hendrik and Bernstein, Abraham},
booktitle={Proceedings of the 17th ACM Conference on Recommender Systems},
pages={813--819},
year={2023}
}
```
- [Benefits of Diverse News Recommendations for Democracy: A User
Study](https://www.tandfonline.com/doi/full/10.1080/21670811.2021.2021804),
Heitz *et al.*, Digital Journalism, 10(10): 1710--1730, 2022.
```tex
@article{heitz2022benefits,
title={Benefits of diverse news recommendations for democracy: A user study},
author={Heitz, Lucien and Lischka, Juliane A and Birrer, Alena and Paudel, Bibek and Tolmeijer, Suzanne and Laugwitz, Laura and Bernstein, Abraham},
journal={Digital Journalism},
volume={10},
number={10},
pages={1710--1730},
year={2022},
publisher={Taylor \& Francis}
}
```
## Contributing
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](https://github.com/Informfully/Documentation).
- Suggest new features in [Future Release](https://github.com/orgs/Informfully/projects/1).
- Make changes and create pull requests.
Please post your feature requests and bug reports in our [GitHub issues](https://github.com/Informfully/Documentation/issues) section.
## License
Released under the [MIT
License](https://github.com/Informfully/Documentation/blob/main/LICENSE).
(Please note that the respective copyright licenses of third-party
libraries and dependencies apply.)