As political attitudes have diverged ideologically in the United States,
political speech has diverged lingusitically. The ever-widening polarization
between the US political parties is accelerated by an erosion of mutual
understanding between them. We aim to make these communities more
comprehensible to each other with a framework that probes community-specific
responses to the same survey questions using community language models
CommunityLM. In our framework we identify committed partisan members for each
community on Twitter and fine-tune LMs on the tweets authored by them. We then
assess the worldviews of the two groups using prompt-based probing of their
corresponding LMs, with prompts that elicit opinions about public figures and
groups surveyed by the American National Election Studies (ANES) 2020
Exploratory Testing Survey. We compare the responses generated by the LMs to
the ANES survey results, and find a level of alignment that greatly exceeds
several baseline methods. Our work aims to show that we can use community LMs
to query the worldview of any group of people given a sufficiently large sample
of their social media discussions or media diet.

利用社区语言模型，研究政治言论语言化的分歧，通过观察群体在社交媒体中的表达，详细分析不同派别的观点，并且发现了相对于其他方法更高的对比度。