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.
Code Repository
GitHub: https://github.com/infosenselab/frameref
Citing FrameRef
If you use this resource in your projects, please cite the following paper.
@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}
}