Semantic shift in social networks (2021)

Abstract: Just as the meaning of words is tied to the communities in which they are used, so too is semantic change. But how does lexical semantic change manifest differently across different communities? In this work, we investigate the relationship between community structure and semantic change in 45 communities from the social media website Reddit. We use distributional methods to quantify lexical semantic change and induce a social network on communities, based on interactions between members. We explore the relationship between semantic change and the clustering coefficient of a community’s social network graph, as well as community size and stability. While none of these factors are found to be significant on their own, we report a significant effect of their three-way interaction. We also report on significant word-level effects of frequency and change in frequency, which replicate previous findings.

## introduction the mechanisms and patterns of semantic change have a long history of study in linguistics (e.g., @xcite @xcite . however, historical accounts of semantic change typically consider meaning at the language level and, as @xcite points out, referring to @xcite account of convention, the meaning of a word "does not hold for a word simpliciter, but for a word in a particular community". this gives rise questions of how semantic change manifests differently in different communities. in this work, we explore relationship between semantic change and several community characteristics, including social network structure. social network analysis has long been a tool of sociolinguists studying variation and change (e.g., @xcite @xcite ), but our work differs somewhat from that tradition in both methodology and focus. sociolinguists typically work with the social networks of individuals-their ego networks-how many people each speaker is connected to, what kind of relationships they have and, sometimes, how people in their immediate network are connected to each other. the ego network is convenient for sociolinguists using ethnographic methods; it is usually infeasible to recreate the entire social network of a large community @xcite . by studying online communities, we are able to define and compute several community-level structural characteristics including size, stability, and social network clustering (section 5). another way that our work differs from the variationist approach is that we consider change on the level of meaning. with a few exceptions (e.g., @xcite , sociolinguistic research studies variation in linguistic form (phonology, morphology and syntax). indeed, mainstream sociolinguists have expressed skepticism that semantics can be a proper subject of variational analysis at all @xcite , since the received definition of linguistic variation concerns multiple forms expressing the same content-i.e., different ways of saying the same thing. with semantics at the top of the traditional linguistic hierarchy, there is no higher-order constant to which two meanings can refer. in this work, we instead consider semantic shift, which refers to changes in the meaning of a given lexical form @xcite . for more traditional sociolinguistic variables, social indexicality-the association of a variant with social identities and ideology-is the main factor that mediates diffusion @xcite . since semantic variation can itself carry social and idealogical meaning @xcite , there is good reason to think that it may be sensitive to some of the same aspects of community structure. the focus on semantic shift is also made possible by computational methodology-we model word meaning with distributional semantics (section 4), which allows us to quantify short-term lexical semantic shifts at the community level. in this study, we model the social networks of 45 english-language communities from the social media website reddit, foot_0 and use diachronic word vectors to measure semantic change between two time periods one year apart. then, we use a multistage linear mixed effects statistical model to test the effect of various community features on wordlevel semantic change. ## related work in this section, we review work that uses computational methods to study linguistic variation and change in social context. distributional semantics distributional methods, which model the meaning of a word with the contexts in which it appears, are a popular way to detect and quantify semantic change. foot_1 several recent studies use distributional semantics to examine short-term semantic shift at the community level. @xcite use diachronic word vectors to study semantic change in political and media discourse, including in uk parliamentary debates, finding that word meaning changes differently depending on the political viewpoint of the speaker. @xcite use diachronic word vectors to measure semantic change in the vkontakte social network during the russia-ukriaine crisis and find that changes in word frequency are predictive of semantic shift @xcite studied short-term semantic shift in the /r/liverpoolfc community on reddit, empirically validating the diachronic word vector model proposed by @xcite by correlating cosine distance between vectors from two different time periods with semantic change judgments collected from members of the community. in another @xcite find variations in word meaning across different reddit communities, including communities organized around the same topic. social network analysis in an early example of using social network analysis to study the language online communities, @xcite categorizes the relationships of users of an irc channel as strong or weak ties, based on interaction frequency. they find that tie strength predicts the use of some online and community-specific forms but not others and conjecture that this difference is related the social meaning of those forms. @xcite examined early twitter conventions for attributing the source tweet to someone else (i.e., indicating that it is a retweet). they examined social network features, such as the size of a user's ego network, but did not find such features to be very predictive of convention adoption compared to global trends. communication games in a laboratory setting have also been used to examine the effect of social network structure on linguistic change. @xcite quantified the communicative success, systematicity and stability of languages developed by "communities" of participants, but did not find a significant effect across the three different network structures that were tested. @xcite found that individuals with larger real-world ego networks had less malleable semantic representations in the lab, and use computer simulations to argue that individuals with smaller ego networks therefore play an important role in the communitylevel propagation of linguistic change. ## data to investigate semantic change in different communities, we use comments collected from the social media website reddit. 3 on reddit, users create posts, which consist of a link, image, or user-generated text, along with a comment section. comments are threaded: users can comment on the post or reply to another user's comment. reddit is divided into forums called subreddits, which are typically organized around a topic of interest. while some forums-especially those organized around relatively niche topics-have a small tightly-knit community of users, others have a much looser community structure, with any given user posting and commenting infrequently. our dataset consists of comments from 45 randomly selected subreddits that were active in the years 2015-2017. in addition to the subreddit corpora, we created a generic reddit corpus, consisting of comments sampled from every subreddit, including communities not in our sample. for both the generic corpus and the community-specific corpora, we constructed separate datasets for 2015 and 2017, leaving a one-year gap between them. the generic corpus consists of 55m comments for 2015 and 54m for 2017. for each of the selected subreddits, we sampled comments from 2015 and 2017 to construct two datasets of 5.4m tokens each (averaging 158k comments). 4 ## semantic change model in this section, we describe how we quantify semantic change. we adopt a modeling procedure similar to that of @xcite , which is adapted from @xcite 's diachronic skip-gram with negative sampling (sgns) model (section 4.1). we define naïve cosine change for the communityspecific and "generic" lexicons (section 4.2). in section 4.3, we use a control procedure adapted from @xcite to account for noise in the naïve metric. ## community features in this section we characterize the structural features of the online communities in our dataset. many of the features we define use the notion of active members. for a community c and time period t, the active members, u c,t , is the set of members who made at least 10 posts in that period. size the size of a community may have an effect on semantic change. in communication game experiments, @xcite found that larger communities of participants developed linguistic structure faster and more consistently than when they were grouped in smaller communities. we define community size, s 2015 = |u c,2015 |, as the number of active members in 2015. stability community stability may also have an effect on semantic change. for example, communi- l p t a ba b a j ea p edh a a a a ka a k a i a a a a a t xc g a o mls ma u a t a k a s a p a a w a a a a a fa a d a 4 cfb ga d a a a a wa a mma 0 0.05 0.1 0.15 0.2 0.25 20 40 60 80 100 120 140 0.06 0.08 0.1 0.12 0.14 0.16 c a (ja a ) c (2015) m a a (2015) ties with stable membership have a better chance of building up community-specific common ground. on the other hand, stable communities may experience less change if such change tends to come from new community members, as some studies have suggested @xcite . we define community stability as the jaccard index between the sets of active members in 2015 and 2017. this metric, ranging from 0 to 1, captures how similar the community membership is between the two time periods. mean posts p 2015 is the average number of posts per active members over the course of 2015. ## predictive model we perform an exploratory analysis of the data using multi-stage regressions and model selection by backwards elimination with semantic change, as measured by ⇤ , as the dependent variable. 7 since we fit the mixed effects model at the word level, in addition to the community-level independent variables described in section 5, we consider two word-level features as fixed effects. see word frequency since word frequency known to interact with semantic change @xcite . we include the frequency of the token in the 2015 community corpus (f 2015 ) as a feature. change in frequency additionally, we include the change in frequency between 2015 and 2017 (@xmath0. effect varies by mean posts (2015) p2015 community size (2015) s2015 community stability t community clustering c community frequency (2015) f2015 token, community change in frequency f token, community generic rectified change ⇤ g token rectified change ⇤ token, community community intercepts in addition to fixed effects, we use community-level random intercepts under the hypothesis that community topics have idiosyncratic reasons or lexical reasons for differences in semantic change rates to do with the community topics themselves, which we do not model. ## discussion and conclusions we conducted an exploratory statistical analysis of the relationship between semantic change and several word-and community-level predictive features. rectified semantic change, our independent variable, protects the results from certain systematic biases inherent in the traditional cosine change metric. by looking at online communities, we were able to compute a clustering coefficient on the social network graph of each community, as well as several other community-level structural features.