| language: | |
| - en | |
| license: cc-by-sa-3.0 | |
| task_categories: | |
| - text-generation | |
| ## Dataset Information | |
| Information ecosystems increasingly shape how people internalize exposure to adverse digital experiences, raising concerns about the long-term consequences for *information health*. In modern search and recommendation systems, ranking and personalization policies play a central role in shaping such exposure and its long-term effects on users. To study these effects in a controlled setting, we present *FrameRef*, a large-scale dataset of 1,073,740 systematically reframed claims across five framing dimensions: *authoritative*, *consensus*, *emotional*, *prestige*, and *sensationalist*, and propose a simulation-based framework for modeling sequential information exposure and reinforcement dynamics characteristic of ranking and recommendation systems. For full details, see the accompanying paper [here](https://arxiv.org/abs/2602.15273). | |
| ## Code Repository | |
| GitHub: https://github.com/infosenselab/frameref | |
| ## Citing FrameRef | |
| If you use this resource in your projects, please cite the following paper. | |
| ```bibtex | |
| @misc{De_Lima_FrameRef_A_Framing_2025, | |
| author = {De Lima, Victor and Liu, Jiqun and Yang, Grace Hui}, | |
| doi = {10.48550/arXiv.2602.15273}, | |
| title = {{FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health}}, | |
| url = {https://arxiv.org/abs/2602.15273}, | |
| year = {2025} | |
| } | |
| ``` |