Individuals signal aspects of their identity and beliefs through linguistic
choices. Studying these choices in aggregate allows us to examine large-scale
attitude shifts within a population. Here, we develop computational methods to
study word choice within a sociolinguistic lexical variable -- alternate words
used to express the same concept -- in order to test for change in the United
States towards sexuality and gender. We examine two variables: i) referents to
significant others, such as the word "partner" and ii) referents to an
indefinite person, both of which could optionally be marked with gender. The
linguistic choices in each variable allow us to study increased rates of
acceptances of gay marriage and gender equality, respectively. In longitudinal
analyses across Twitter and Reddit over 87M messages, we demonstrate that
attitudes are changing but that these changes are driven by specific
demographics within the United States. Further, in a quasi-causal analysis, we
show that passages of Marriage Equality Acts in different states are drivers of
linguistic change.

本文研究了在美国社会语言学的词汇选择中关于同性恋和性别的态度变化，通过在 Twitter 和 Reddit 上对 8700 万条消息的纵向分析，发现态度正在发生变化但是这些变化是由特定人口群体驱动的，并且在溯因分析中表明不同州的婚姻平等法案是语言变化的推动因素。

利用社会语言学变量揭示性取向和性别态度的变化

Using Sociolinguistic Variables to Reveal Changing Attitudes Towards  Sexuality and Gender

Computer-mediated communication is driving fundamental changes in the nature
of written language. We investigate these changes by statistical analysis of a
dataset comprising 107 million Twitter messages (authored by 2.7 million unique
user accounts). Using a latent vector autoregressive model to aggregate across
thousands of words, we identify high-level patterns in diffusion of linguistic
change over the United States. Our model is robust to unpredictable changes in
Twitter's sampling rate, and provides a probabilistic characterization of the
relationship of macro-scale linguistic influence to a set of demographic and
geographic predictors. The results of this analysis offer support for prior
arguments that focus on geographical proximity and population size. However,
demographic similarity -- especially with regard to race -- plays an even more
central role, as cities with similar racial demographics are far more likely to
share linguistic influence. Rather than moving towards a single unified
"netspeak" dialect, language evolution in computer-mediated communication
reproduces existing fault lines in spoken American English.

通过分析 107 百万条推特信息，我们发现计算机媒介通信正在驱动书面语言的根本变革，语言演化并不使得美国英语变得一致，而是重现了口语中现有的毛病。