Understanding the framing of political issues is of paramount importance as
it significantly shapes how individuals perceive, interpret, and engage with
these matters. While prior research has independently explored framing within
news media and by social media users, there remains a notable gap in our
comprehension of the disparities in framing political issues between these two
distinct groups. To address this gap, we conduct a comprehensive investigation,
focusing on the nuanced distinctions both qualitatively and quantitatively in
the framing of social media and traditional media outlets concerning a series
of American Supreme Court rulings on affirmative action, student loans, and
abortion rights. Our findings reveal that, while some overlap in framing exists
between social media and traditional media outlets, substantial differences
emerge both across various topics and within specific framing categories.
Compared to traditional news media, social media platforms tend to present more
polarized stances across all framing categories. Further, we observe
significant polarization in the news media's treatment (i.e., Left vs. Right
leaning media) of affirmative action and abortion rights, whereas the topic of
student loans tends to exhibit a greater degree of consensus. The disparities
in framing between traditional and social media platforms carry significant
implications for the formation of public opinion, policy decision-making, and
the broader political landscape.

通过定性和定量研究美国最高法院关于平权行动、学生贷款和堕胎权的系列裁决，发现社交媒体和传统媒体在政治问题的框架中存在重要差异和极化，对公众舆论、政策决策和整个政治环境的形成产生重大影响。

理解最高法院争议的差异框架：社交媒体 vs 新闻机构

Understanding Divergent Framing of the Supreme Court Controversies:  Social Media vs. News Outlets

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.

通过大规模的调整语言模型，提出了一种建模差异语境的框架，该框架能够捕捉传统媒体和社交媒体上政治性偏见问题的高度可靠性。

通过遮蔽语言建模捕捉主题框架

Capturing Topic Framing via Masked Language Modeling

In this paper we present the ClaimBuster dataset of 23,533 statements
extracted from all U.S. general election presidential debates and annotated by
human coders. The ClaimBuster dataset can be leveraged in building
computational methods to identify claims that are worth fact-checking from the
myriad of sources of digital or traditional media. The ClaimBuster dataset is
publicly available to the research community, and it can be found at
this http URL

本文介绍了 ClaimBuster 数据集，该数据集包含 23533 条来自美国总统大选辩论的语句，并由人工编码进行了注释。可以利用 ClaimBuster 数据集构建计算机方法，识别值得进行事实核查的声明。ClaimBuster 数据集可供研究社区公开使用。