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.

翻译：信息生态系统日益塑造人们如何内化对不良数字体验的暴露，引发了关于信息健康长期影响的担忧。在现代搜索与推荐系统中，排序与个性化策略在塑造此类暴露及其对用户的长期影响方面发挥着核心作用。为在受控环境中研究这些效应，我们提出了FrameRef——一个包含1,073,740条跨五个框架维度（权威性、共识性、情感性、声望性、煽动性）的系统性重构主张的大规模数据集，并提出了一个基于仿真的框架，用于建模排序与推荐系统特有的序列化信息暴露与强化动态。在该框架内，我们通过采用框架条件损失衰减的微调方法构建语言模型，从而创建对框架敏感的智能体角色，在保持整体任务能力的同时诱导特定认知偏差。通过蒙特卡洛轨迹采样，我们证明接受度与置信度的微小系统性偏移会随时间累积，导致累积信息健康轨迹出现显著分化。人工评估进一步证实FrameRef生成的框架可量化地影响人类判断。我们的数据集与框架共同为通过仿真开展系统性信息健康研究奠定了基础，对负责任的人本中心研究形成补充与启发。我们在https://github.com/infosenselab/frameref发布了FrameRef数据集、代码、文档、人工评估数据及角色适配器模型。