FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health
arXiv:2602.15273v1 Announce Type: cross 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 compete
arXiv:2602.15273v1 Announce Type: cross 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.
Executive Summary
The article introduces FrameRef, a comprehensive dataset and simulation framework designed to study the long-term effects of information exposure on users' information health. With 1,073,740 reframed claims across five dimensions—authoritative, consensus, emotional, prestige, and sensationalist—FrameRef offers a controlled environment to model sequential information exposure and reinforcement dynamics in search and recommendation systems. The study demonstrates that small, systematic shifts in acceptance and confidence can lead to significant divergence in cumulative information health trajectories. Human evaluations confirm the impact of generated framings on human judgment, highlighting the potential of FrameRef for advancing research in information health.
Key Points
- ▸ FrameRef dataset includes 1,073,740 systematically reframed claims across five dimensions.
- ▸ Simulation framework models sequential information exposure and reinforcement dynamics.
- ▸ Small shifts in acceptance and confidence can lead to substantial divergence in information health trajectories.
- ▸ Human evaluations confirm the impact of generated framings on human judgment.
Merits
Comprehensive Dataset
The extensive dataset provides a robust foundation for studying the effects of framing on information health, offering a wide range of reframed claims across multiple dimensions.
Innovative Simulation Framework
The simulation framework allows for controlled experiments on information exposure and reinforcement dynamics, which is crucial for understanding long-term effects on users.
Empirical Validation
Human evaluations provide empirical evidence that the generated framings significantly affect human judgment, enhancing the credibility of the findings.
Demerits
Complexity of Simulation
The complexity of the simulation framework may pose challenges for researchers without specialized knowledge in machine learning and data analysis.
Generalizability of Findings
While the dataset is extensive, the generalizability of the findings to real-world scenarios may be limited due to the controlled nature of the simulations.
Ethical Considerations
The potential for misuse of the dataset and framework in manipulating information health trajectories raises ethical concerns that need to be addressed.
Expert Commentary
The article presents a significant advancement in the study of information health, offering a rigorous and innovative approach to understanding the long-term effects of framing on user judgment. The comprehensive dataset and simulation framework provide a valuable tool for researchers to explore the complexities of information exposure and reinforcement dynamics. The empirical validation through human evaluations adds a layer of credibility to the findings, demonstrating the real-world impact of generated framings. However, the complexity of the simulation and the ethical considerations surrounding the potential misuse of the framework are important caveats. The study's focus on algorithmic bias and digital well-being aligns with current debates in the field, making it a timely and relevant contribution. The practical and policy implications of the research are substantial, offering actionable insights for both practitioners and policymakers. Overall, the article sets a high standard for future research in information health and underscores the need for responsible and ethical approaches in the design of digital information systems.
Recommendations
- ✓ Future research should explore the generalizability of the findings to diverse populations and real-world scenarios to enhance the applicability of the results.
- ✓ Ethical guidelines should be developed and adhered to when using the FrameRef dataset and simulation framework to prevent misuse and ensure responsible research practices.
