  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.
