Differential framing of issues can lead to divergent world views on important issues. This is especially true in domains where the information presented can reach a large audience, such as traditional and social media. Scalable and reliable measurement of such differential framing is an important first step in addressing them. In this work, based on the intuition that framing affects the tone and word choices in written language, we propose a framework for modeling the differential framing of issues through masked token prediction via large-scale fine-tuned language models (LMs). Specifically, we explore three key factors for our framework: 1) prompt generation methods for the masked token prediction; 2) methods for normalizing the output of fine-tuned LMs; 3) robustness to the choice of pre-trained LMs used for fine-tuning. Through experiments on a dataset of articles from traditional media outlets covering five diverse and politically polarized topics, we show that our framework can capture differential framing of these topics with high reliability.

翻译：议题的差异化框架可能导致人们对重要议题产生分歧的世界观。这在传统媒体和社交媒体等信息传播范围广泛的领域中尤为突出。对这种差异化框架进行可扩展且可靠的测量，是解决这些问题的重要第一步。基于框架影响书面语言语气和词汇选择这一直觉，本研究提出一个通过大规模微调语言模型进行掩码标记预测来建模议题差异化框架的框架。具体而言，我们探讨了该框架的三个关键因素：1) 用于掩码标记预测的提示生成方法；2) 微调语言模型输出的归一化方法；3) 对微调所用预训练语言模型选择的鲁棒性。通过在涵盖五个多样化且政治极化话题的传统媒体文章数据集上开展实验，我们证明该框架能够以高可靠性捕捉这些话题的差异化框架。