The structure of online social networks modulates the rate of lexical change (2021)

Abstract: New words are regularly introduced to communities, yet not all of these words persist in a community’s lexicon. Among the many factors contributing to lexical change, we focus on the understudied effect of social networks. We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade. Using Poisson regression and survival analysis, our study demonstrates that the community’s network structure plays a significant role in lexical change. Apart from overall size, properties including dense connections, the lack of local clusters, and more external contacts promote lexical innovation and retention. Unlike offline communities, these topic-based communities do not experience strong lexical leveling despite increased contact but accommodate more niche words. Our work provides support for the sociolinguistic hypothesis that lexical change is partially shaped by the structure of the underlying network but also uncovers findings specific to online communities.

## introduction lexical change is a prevalent process, as new words are added, thrive, and decline in day-to-day usage. while there is a certain randomness at play in word creation and adoption @xcite , there are also psychological, social, linguistic and evolutionary factors that systematically affect lexical change @xcite @xcite . in sociolinguistics, one structural factor that has long been recognized as influencing lexical changes is the language community's social network. for example, drawing on pioneering works on social networks @xcite , the weak tie model of change holds that the structural properties of social networks can account for the general tendency of some language communities to be more resistant to linguistic change than oth-ers @xcite @xcite . a classic finding is that loose-knit networks with mostly weak ties are more conducive to information diffusion, thereby facilitating innovation and change, while close-knit networks with strong bonds impose norm-enforcing pressure on language usage, strengthening the localized linguistic norms @xcite . one compelling observation in favor of this argument concerns the comparison between two germanic languages, icelandic and english. icelandic has changed little since the late thirteenth century, which could be due to the norm-enforcing pressure inherent in the strong kinship and friendship ties. in contrast, in early modern london english, the loosening of network ties, accompanied by the rise of the mobile merchant class, was argued to be responsible for some radical change in the language @xcite . this study extends network-based sociolinguistic research to online communities, which remain understudied despite their expansion in past decades. while we draw an analogy between offline and online communities, our focus is on communities of practice @xcite @xcite , or "an aggregate of people who come together around mutual engagement in an endeavor" @xcite , rather than offline speech communities. we examine how network structures affect lexical innovation, retention and levelling in online communities. specifically, we ask 1) how network structure contributes to the introduction of new words to online communities (innovation), 2) how structural properties affect the survival of these newly introduced words (retention) and 3) whether the increased inter-connectedness causes online communities to adopt a similar set of new words (levelling). this work offers the following contributions. first, using a massive longitudinal dataset of 4420 figure 1 : applying the hypothesis of @xcite to these two gaming subreddits of similar size suggests that the network with lower density (left; r/masseffect) will be more innovative than the more closelyconnected community shown right (r/f13thegame). however, after controlling for size, the one with higher average degree (more inner-connections) (right: r/f13thegame) tends to develop more lexical innovations. ## lexical change lexical change and social networks since the landmark study of sound change in the belfast community by @xcite , the impact of network structures on language change has been a key consideration in sociolinguistics. @xcite found that speakers in loose-knit networks tend to experience more linguistic change than those in close-knit networks. most early social network studies focus predominantly on speakers in local, less mobile communities where ties between people tend to be strong @xcite @xcite . except for a few recent simulation studies @xcite , researchers have rarely explored how the global properties of social networks systematically affect lexical change, although the weak tie model does predict an influence of social network at the macro-level. in addition, while there are lexicographic studies attempting to enumerate factors that affect the acceptance of neologisms @xcite , network structures are rarely taken into consideration. a key limitation of previous works has been access to a large longitudinal dataset of communities with different network properties as well as a precise estimate of the network structure of larger communities, which are limitations this study overcomes. ## the reddit network corpus to analyze lexical innovation in a network setting across long time scales, we use comments made to reddit, one of the most popular social media sites. there, 330m users are active in about 1m distinct topic-based sub-communities (subreddits). here we define each subreddit as a community of practice @xcite , as each subreddit is relatively independent with various norms formed through interactions. the subreddit communities span across a wide range of social network structures @xcite and linguistic use patterns @xcite , making them ideal for studying the propagation of sociolinguistic variations in online communities. detailed statistics are given in appendix b. data to strike a balance between acquiring active subreddits and preserving the diversity of these communities, we initially select the top 4.5k subreddits based on their overall size from their inception to october 2018 via the convokit package @xcite . let c @xmath0, c 2 , . . . , c n } be the set of subreddit communities included in the corpus. a subreddit community c n is further discretized into multiple monthly subreddit communities c n (t) based on its actual life span in the monthly time step t, such that c @xmath1, c n (2), . . . , c n (t max )}. for each c n (t), we extracted all individual comments except those marked as [deleted] and performed tokenization via spacy. during text cleaning, we removed numbers, emojis, urls, punctuations and stop words, and set a cutoff frequency of 10 over the entire dataset to exclude infrequent typos or misspellings. only those monthly subreddits c n (t) with more than 500 words or 50 users after preprocessing are retained. some communities known for their content in foreign languages are also removed. after preprocessing, 4420 subreddits were left in our analysis. community networks for a community c from month @xmath2, 2, . . . , t max , its temporal network can be represented as a discretetime sequence of network snapshots and a set of edges e c (t) characterizing direct interactions between users. g c (t) is initiated as an undirected and unweighted graph under the assumption that these commenting communications are mutual and bi-directional. a user u i is represented as a node if this user has posted at least one comment at month t. an edge e ij exists between user u i and user u j if these two users have interacted in close proximity in a common discussion thread, that is, separated by at most two comments @xcite . since online communications are asynchronous, a discussion thread created at time t may still have active comments from users at time t + 1 or later. for such threads, we only included interactions at time t in g c (t) and grouped later interactions into the future time steps at which these interactions happened. users marked as @xcite or automoderater were all removed. after filtering, a total of 289.8k community networks have been extracted for all 4420 communities. ## network statistics communities in reddit can be defined in terms of how their members relate within the community (intra) and how the community relates to other communities (inter) through multi-community memberships by its users @xcite . we formalize both as potential influences. as network attributes may be affected by the hyperparameters for network construction, we additionally validate this approach in appendix c. ## lexical innovations in what types of communities are neologisms likely to be introduced? here, we investigate the extent to which the number of innovations introduced per month can be predicted with only the structural properties of community networks. ## survival analysis not all lexical innovations survive through time, with only a few neologisms eventually becoming widely adopted by community members. here, we test the structural factors that systematically affect the survival of words in online communities. model specification survival analysis models the elapsed time before a future event happens (kleinbaum and @xcite , which has been used to predict word survival @xcite . compared to the traditional cox model, deep survival analysis approximates the risk (hazard) with neural networks, thereby achieving improved performance. we estimated word survival with the logistic hazard model (lh) proposed by @xcite . given samples {x 1 , x 2 , . . . , x n } and time steps {1, 2, . . . , t }, the lh method estimates h(t|x), the hazard function of the death event with respect to time t, with a deep neural network. the hazard function can be interpreted as the word's "danger of dying" at t. after the model is trained, the survival function s(t|x i ) for sample x i can be computed as s(t|x i ) can be interpreted as the chance of survival at time t for sample x i , that is, the survival probability of a word given the corresponding network features at time t. the detailed derivation and experiment settings are given in appendix f. ## lexical levelling levelling refers to the gradual replacement of localized linguistic features (marked) by mainstream linguistic features (unmarked) over the whole community @xcite , which has been observed in a wide range of offline linguistic communities due to increasing mobility and external contacts @xcite . the subreddit communities have become increasingly inter-connected over time, as the average inter-community degree has increased from 6 in january 2008 to 2,323 in october 2018 (figure 5 ). while some of these could be accounted for by the simultaneous growth in the number of subreddits, the growth in connectedness is also apparent. such an increase of contact could promote the spread of neologisms across reddit. in the same period, the number of variants that spread to more than 60% of the communities has grown slightly from 7 to 22. some of the notable examples include words like lol, alot, imao and cuz. meanwhile, the variants that are only confined to one community grew rapidly from 1992 in 2008 to 23,397 in 2018. the widespread use of some neologisms does not necessarily cause the loss of local expressions, as in offline communities. instead, the communityspecific terms and community-general terms develop in tandem. many community-specific terms are nested within topic-based communities with little meaning overlap with those widespread variants, and are therefore unlikely to be replaced by more general terms through levelling. figure 5 also shows that the probabilistic density distribution (pdf) of word dissemination (the percentage of communities sharing a neologism) conforms to the power law fit p(x) ∝ x -α , as a few words spread to most communities while most words are confined to a few communities. further, the shape parameter α decreases asymptotically despite the growth of average inter-community degree (figure 5 ), which implies that, as the size of reddit grows, more community-specific words, as well as more widespread words, emerge. ## discussions and conclusions in traditional sociolinguistics, weak ties within a social network have been linked to innovation and language change. yet most studies only use indirect evidence to infer the underlying network types @xcite @xcite . our quantitative analysis suggests that multiple structural properties play a role in lexical change. the overall network size is the most prominent factor in lexical innovation and survival, as large communities provide the base population to create and use those neologisms. the effect of network size has also been emphasized in other network studies of language @xcite @xcite . however, sheer size is only part of the story, as dense edges between users, the lack of separate local clusters, and rich external connections also promote both lexical innovation and survival. dense connections within and across communities increase the visibility of neologisms so that they can be imitated by other users, as exposure alone predicts users' information spreading behavior @xcite . in contrast, local clustering tends to separate networks into disconnected parts, slowing the spread of new words. these structural attributes are found to facilitate information spread in online social networks @xcite . on a broader scale, our results suggest that the lexical change process in online social networks may be similar to other information spread processes @xcite . our results show that conclusions drawn from offline communities might be insufficient to account for behavior seen in online social networks. while the classic weak tie model emphasizes the role of loose social networks in language change @xcite and has been confirmed in online communities @xcite , our work further extends this model by showing that a variety of network structural attributes also play a role in language change. our quantitative analysis also suggests a different leveling process in online communities with implications for sociolinguistic theories. limitations and future work one limitation of this study is that topical variation is not explored in depth, because we aimed to look at the contributes of networks alone by smoothing out topical variation with diverse communities. yet topics have been found to affect users' posting behavior in online communities @xcite and niche topics do affect word retention @xcite . in reddit, communities involving certain niche or foreign topics, such as r/pokemon, might inherently introduce more lexical innovations than others. secondly, we only focus on internet neologisms in reddit. how these neologisms propagate across multiple social media platforms and how online and offline neologisms interact remain important questions to be addressed. thirdly, while our study reveals the general patterns of lexical change, there are multiple sub-categories of neologisms such as discourse markers and name entities. it is of interest to ask whether different subcategories may exhibit different patterns of usage in online communities. these research questions are worth exploring in future work.