FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health

TL;DR

FrameRef introduces a large dataset and simulation framework to study how framing influences information health over time, enabling controlled experiments on the effects of digital content exposure.

Contribution

The paper presents a novel dataset of reframed claims and a simulation testbed for modeling the impact of framing on information health dynamics.

Findings

Small framing shifts can lead to large divergence in information health trajectories.

Generated framings significantly influence human judgment.

Simulation reveals reinforcement effects in sequential information exposure.

Abstract

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.