FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health
This work addresses information health concerns for users in digital ecosystems, providing a simulation-based foundation for research, though it is incremental in building on existing methods for controlled studies.
The authors tackled the problem of modeling how ranking and recommendation systems affect long-term information health by introducing FrameRef, a dataset of over 1 million reframed claims and a simulation framework, showing that small systematic shifts in acceptance and confidence can lead to substantial divergence in cumulative information health trajectories.
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. Within this framework, we construct framing-sensitive agent personas by fine-tuning language models with framing-conditioned loss attenuation, inducing targeted biases while preserving overall task competence. Using Monte Carlo trajectory sampling, we show that small, systematic shifts in acceptance and confidence can compound over time, producing substantial divergence in cumulative information health trajectories. Human evaluation further confirms that FrameRef's generated framings measurably affect human judgment. Together, our dataset and framework provide a foundation for systematic information health research through simulation, complementing and informing responsible human-centered research. We release FrameRef, code, documentation, human evaluation data, and persona adapter models at https://github.com/infosenselab/frameref.