Welcome to Informfully (GitHub & Website)! Informfully is an open-source reproducibility platform for content distribution and user experiments.
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The Informfully Dataset with Enhanced Attributes (IDEA) for news articles. Recommendations comprise an open-source collection of user profiles, news articles with high topic and outlet diversity, item recommendations, and rich user-item interactions from a field study on behavioral changes in news consumption. The records include both quantitative data from real-time session tracking as well as self-reported data from user surveys on satisfaction with news, knowledge acquisition, and personal background information.
This paper outlines the data collection procedure and potential use cases of the dataset for designing normative recommender systems. It provides the documentation of all data collections, together with insights into the data quality. You can download the full paper here: IDEA - Informfully Dataset with Enhanced Attributes
- Nudges for News Recommenders journal paper based on this dataset.
- IDEA – Informfully Dataset with Enhanced Attributes workshop paper based on this dataset.
- Dataset codebook for the description of all attributes included in the dataset.
- Pre-processing scripts used for creating the individual files shared in the dataset.
- Technical documentation of outlining all document collections from Informfully.
- Experiment pre-registration 1 for information related to the first week.
- Experiment pre-registration 2 for information related to the second week.
| Collection | Description | # Entries |
|---|---|---|
| Articles | News article collection. | 10,954 |
| Bookmarks | Bookmarked news articles. | 2,479 |
| Favorites | Favorites news articles. | 3,115 |
| Interactions | Read articles by users. | 34,890 |
| Ratings | Likes and dislikes for articles. | 28,382 |
| Recommendations | Daily article recommendations. | 207,220 |
| Survey | Knowledge quiz answers. | 43,078 |
| Users | Profile and background information. | 593 |
| Views | Browsing and session history. | 84,747 |
If you are looking for more news datasets, we recommend the following resources:
If you use any code or data from this repository in a scientific publication, we ask you to cite the following papers:
-
Nudges for News Recommenders, Mattis et al., Journal of Communication, 2025.
@article{mattis2025nudges, title={Nudges for News Recommenders: Prominent Article Positioning Increases Selection, Engagement, and Recall of Environmental News, but Reducing Complexity Does Not}, author={Mattis, Nicolas and Heitz, Lucien and Masur, Philipp K and Moeller, Judith and van Atteveldt, Wouter}, journal={Journal of Communication}, pages={jqaf019}, year={2025}, publisher={Oxford University Press, UK}, url={https://doi.org/10.1093/joc/jqaf019} } -
IDEA – Informfully Dataset with Enhanced Attributes, Heitz et al., Proceedings of the Second Workshop on the Normative Design and Evaluation of Recommender Systems, 2024.
@inproceedings{heitz2024idea, title={IDEA – Informfully Dataset with Enhanced Attributes}, author={Heitz, Lucien and Mattis, Nicolas and Inel, Oana and van Atteveldt, Wouter}, booktitle={Proceedings of the Second Workshop on the Normative Design and Evaluation of Recommender Systems}, year={2024}, url={http://ceur-ws.org/Vol-3898/paper1.pdf} } -
Informfully Recommenders – Reproducibility Framework for Diversity-aware Intra-session Recommendations, Heitz et al., Proceedings of the 19th ACM Conference on Recommender Systems, 2024.
@inproceedings{heitz2025recommenders, title={Informfully Recommenders – Reproducibility Framework for Diversity-aware Intra-session Recommendations}, author={Heitz, Lucien and Li, Runze and Inel, Oana and Bernstein, Abraham}, booktitle={Proceedings of the 19th ACM Conference on Recommender Systems}, pages={792--801}, year={2025}, publisher={ACM New York, NY, USA}, url={https://doi.org/10.1145/3705328.3748148} }
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.
Released under the MIT License. (Please note that the respective copyright licenses of third-party libraries and dependencies apply.)
