Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender (2021)

Abstract: 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.

## introduction a person's identity and attitudes are reflected in the language they use @xcite @xcite . in particular, the linguistic choice for a concept can reveal the individual's stance or attitudes @xcite ; for example, the use of "illegals" or "undocumented" in reference to immigrants reveals the speaker's attitude on immigration @xcite . these alternations in word choice are known as lexical variables in sociolinguistics. examining the relative frequencies of a variable's words can reveal the underlying attitudes within a † work performed while the author was an undergraduate research assistant at the university of michigan. sociolinguistics has long focused on variation in language with respect to identity and attitudes @xcite @xcite . recent computational studies have built upon this line of research @xcite , showing not only that this variation occurs in social media @xcite , but also that the large scale of social media enables the study of broader societal trends @xcite . our work expands this line of research by examining longitudinal changes in linguistic variation to show changing societal attitudes. here, we test for change in attitudes about sexuality and gender by computationally measuring variation for two lexical variables associated with these attitudes from a massive longitudinal study of 73m twitter posts and 14m reddit comments across nearly ten years. the first variable focuses on the use of gender when referring to romantic partners; specifically, we test how frequencies in gender-neutral referents such as partner-a term often used by lgbt+ community membersshift as acceptance of gay marriage changes. the second variable measures attitudes about gender through testing for unnecessary gender markings on indefinite references to one or more people, e.g., "some folks" versus "some guys." our work here is drawn from theory in gender and sexuality studies on how both heterosexuality and masculinity are treated as the default or norm in english @xcite , where shifts away from these heterosexual constructs signal increasing acceptance of other identities. our paper offers the following three contributions. first, through a large-scale computational analysis that measures the language choices of different demographics, we demonstrate increasing acceptance of non-heterosexual relationships through the increasing use of non-gendered referents to significant others by heterosexual communities. while non-gendered referents are used frequently in lgbt+ communities, further demographic analysis shows this change is found across gender identities. second, in a quasi-causal analysis, we show that passages of marriage equality acts (mea) in the united states drives a statistically significant increase of gendered markers in the lgbt+ community (e.g., husband instead of partner), mirroring increased acceptance and decreased social cost for explicitly indicating one's sexual orientation @xcite . third, we find increasing gender equality through decreased use of gendered person referents, driven by multiple segments of the population. our work not only reveals positive societal change in acceptance but points to the potential of linguistic variation as indicator variables for studying cultural attitudes. ## sociolinguistic variables sociolinguistic variables consist of alternative expressions where each expression is associated with a specific identity or attitude @xcite . typically, these variables have been pronunciations, e.g., the association of g-dropping with african american vernacular english @xcite , due to the need for high observational frequency within inperson studies in order to identify rigorous associations between form and identity/stance @xcite . the availability of massive quantities of natural text from social media has substantially increased our ability to study lexical variables, which occur less frequently than pronunciation variations @xcite . while many studies have focused on associations between demographics and lexical signals @xcite @xcite , we examine associations between attitudes and two variables: (1) referents to significant others and (2) indefinite referents to one or more persons. we refer to these variables respectively as sigother and person and motivate them next. sigother individuals frequently refer to romantic partners in conversation. signalling the gender of these partners also reveals their sexual orienta-sigother: boyfriend, girlfriend, husband, bae, partner, bf, gf, babe, lover person: people, girl, man, guy, person, girls, guys, dude, bro, individual to focus on interpersonal contexts for the sig-other variable, we restrict all uses of its variants to occur only within a possessive pronoun construction, e.g. my girlfriend/his spouse and later distinguish between first-person and third-person uses, as each carries different social risks. for the person variable, our focus is on contexts where the gender of the referred persons is inherently ambiguous, i.e., a indefinite referent to a person or group; the underlying hypothesis is that gender need not be ascribed to the referent and any ascribed is a result of underlying attitudes and assumption. therefore, we filter uses of the variants to occur immediately after a subset of the determiners, focusing on indefinite articles (e.g. a dude), quantifiers (e.g. most people), distributives (e.g. many folks), difference words (e.g. other pals), as well as broader qualifiers (e.g. if, when). indefinite references to ambiguous persons were chosen as opposed to definite ones (e.g. you guys) as the latter often takes on a specific audience, which could have a known gender composition that necessitates usage over a ungendered form. ## data our work is drawn from two major social media platforms, reddit and twitter, and is focused on english-language conversations in the us. reddit is a major social media platform where individuals participate in communities, known as subreddits, often focused on specific interests, goals, or demographics. reddit users are primarily english-speakers and recent market research suggests its userbase is largely comprised of american users @xcite ; we thus treat content from reddit as reflective of this region's attitudes. on reddit, communities have formed around particular identities associated with known sociolinguistic variation, e.g., r/gaybros. we treat participation in these communities as an implicit signal of an affiliation with that identity, allowing us to study linguistic variation with respect to these identities. here, we identify four categories of subreddits around identity, with 15 total identities across those categories, shown here each with an example: politics right-leaning (r/conservative), left-leaning (r/voteblue); religion general (r/religion), christianity (r/christianity), islam (r/islam), judaism (r/judaism), non-believers (r/atheism); sexuality lgbt+ (r/ainbow), heterosexual (r/relationships); foot_2 and gender transgender (r/transgender), men (r/daddit), women (r/askwomen). 3 a full list of communities and details of the selection process are provided in supplemental material. these categories were chosen based on minimum volume and motivated by prior work showing that individuals modify their language to signal affiliations, attitudes, and beliefs @xcite . data was collected from all content in these communities during 2013-2019, totalling 73m comments. additionally, to test for aggregate changes on the platform, we use a random sample of 5m comments per year, stratified across years. twitter is a major international social media platform. prior work has shown that location data for tweets can be used to identify lexical variation associated with identity @xcite @xcite . in these settings, the demographics associated with the location of a tweet are treated as proxies for the identity of the individual. however most tweets do not come with location data; to increase the sample size, we geocode all tweets from a ∼10% random sample spanning 2011-2019 using the method of @xcite and retain only those present in the united states. this method is known to be the least-biased across urban and rural settings @xcite , allowing us to study all parts of the us. this geocoding had a median error of 8km in our tests; furthermore, we restricted processing to tweets that were marked as english by twitter. to obtain demographic estimates for each tweet's author, we match the inferred location with its containing census tract and use the us census' 2017 american community survey (acs) variable 5-year estimates. the selected acs variables focus on socioeconomic status (ses), and cover income, public assistance, education, unemployment, poverty, income inequality, population density, and age dependency. these demographic variables provide a complementary set of indicators for studying variation compared with reddit. final data a total of 73m tweets from 2011-2019 and 14m reddit comments from 2013-2019 are used. to test for aggregate changes on the platforms, we randomly sample 1m comments per month for reddit (84m total) and twitter (108m total). the filtering process uses nltk for pos tagging and yields 6.7m contexts from reddit and 30m from twitter for the sigother variable; for the person variable yields 7.3m contexts from reddit and 43m from twitter. full details are in supplemental material §a. ## measuring attitudes to sexuality attitudes about sexuality can be signalled in the use of gender in referring to one's significant other. in settings where heterosexual partnerships are valorized, referring to a significant other of the same sex carries a social and potentially-physical risk from admitting to being different from heterosexual practices @xcite . to minimize this risk, some lgbt+ individuals refer to significant others using the variant form partner, which leaves the gender status ambiguous. however, use of partner only by only lgbt+ community members would result in it being a clear marker of a marginalized community. therefore, allies of this community are known to use this word to decrease its association with a marginalized identity (kitchener, 2019). thus, uses of gender markers to refer to significant others reflect underlying attitudes of acceptance towards gay marriage, allowing us to study changes in these attitudes by examining relative rates among variants' uses. here, we ask to what degree is this shift in attitude mirrored in changes in language use, testing two hypotheses. h1: lgbt+ communities will increasingly use gendered terms when referring to relationship status. h2: heterosexual individuals will increasingly use gender-neutral markers in sigother. h1 is motivated by @xcite , who in a small observational study found that lgbt+ individuals preferred using gendered markers, which suggests that use of gender-neutral markers by the lgbt+ members may actually be in the minority. to test these hypotheses, we measure variant use longitudinally and across identities. to avoid larger communities outweighing smaller ones and contributing more to an observed change in progress, in all cross-community comparisons, we controlled for community size by bootstrapping the mean probability within each category of subreddits, and show the 95% confidence intervals in the figures. ## attitudes on gender equality traditionally, standard american english has been gendered in its referents to people, with phrases like "guys" referring to both male and mix-gendered groups @xcite . studies have argued that these marking practices reflect latent bias in gender expectations and reinforce masculinity as the normative gender @xcite . recent efforts have pushed for increased use of genderneutral forms @xcite , where variants like "people" or "folks" are used instead. 4 using our data, we test if these efforts have had an effect and which groups are driving linguistic change. ## conclusion linguistic choices reflect underlying attitudes about what is being discussed. to study attitudes about sexuality and gender equality, we identify lexical variables that reflect these attitudes and computationally study these choices using a massive demographically-labeled corpus of 87m english messages from twitter and reddit. our results show that language use has indeed shifted and points to increasing acceptance of nonheterosexual norms towards inclusive, genderneutral language. through our demographic analysis, we point to key segments of the population driving these changes. further, through a quasicausal analysis, we show that passage of marriage equality acts in different us states increases the use of gendered spousal references, rather than gender-neutral equivalents. while our work does not identify all the underlying causes behind these changes, the results point to where future work could look to identify the structural and social mechanisms behind change and also show how future computational studies can use sociolinguistic variables to tease out demographicallyassociated attitudes. data and code are available at https://github.com/davidjurgens/sociolinguistic-attitudes .