  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.
