Language change is influenced by many factors, but often starts from
synchronic variation, where multiple linguistic patterns or forms coexist, or
where different speech communities use language in increasingly different ways.
Besides regional or economic reasons, communities may form and segregate based
on political alignment. The latter, referred to as political polarization, is
of growing societal concern across the world. Here we map and quantify
linguistic divergence across the partisan left-right divide in the United
States, using social media data. We develop a general methodology to delineate
(social) media users by their political preference, based on which (potentially
biased) news media accounts they do and do not follow on a given platform. Our
data consists of 1.5M short posts by 10k users (about 20M words) from the
social media platform Twitter (now "X"). Delineating this sample involved
mining the platform for the lists of followers (n=422M) of 72 large news media
accounts. We quantify divergence in topics of conversation and word
frequencies, messaging sentiment, and lexical semantics of words and emoji. We
find signs of linguistic divergence across all these aspects, especially in
topics and themes of conversation, in line with previous research. While US
American English remains largely intelligible within its large speech
community, our findings point at areas where miscommunication may eventually
arise given ongoing polarization and therefore potential linguistic divergence.
Our methodology - combining data mining, lexicostatistics, machine learning,
large language models and a systematic human annotation approach - is largely
language and platform agnostic. In other words, while we focus here on US
political divides and US English, the same approach is applicable to other
countries, languages, and social media platforms.

利用社交媒体数据，我们对美国党派的左右分歧进行语言分析，发现在话题、情感和词汇语义方面存在语言差异，并对可能导致语言分化的沟通问题提出警示。