Investigating Online Community Engagement through Stancetaking (2023)

Abstract: Much work has explored lexical and semantic variation in online communities, and drawn connections to community identity and user engagement patterns. Communities also express identity through the sociolinguistic concept of stancetaking. Large-scale computational work on stancetaking has explored community similarities in their preferences for stance markers – words that serve to indicate aspects of a speaker’s stance – without considering the stance-relevant properties of the contexts in which stance markers are used. We propose representations of stance contexts for 1798 Reddit communities and show how they capture community identity patterns distinct from textual or marker similarity measures. We also relate our stance context representations to broader inter- and intra-community engagement patterns, including cross-community posting patterns and social network properties of communities. Our findings highlight the strengths of using rich properties of stance as a way of revealing community identity and engagement patterns in online multi-community spaces.

## introduction communities vary in their language choices, often in ways that are indicative of the community's shared interests, values, and norms -their community identity. sociolinguists refer to such spaces as communities of practice @xcite , and have explored how they vary in their in-person linguistic practices @xcite . computational work has explored variations of word and sense usage in online communities of practice (e.g., @xcite @xcite , highlighting relationships between linguistic and non-linguistic aspects of community identity, including user retention, community size, and network structure. community identity is also expressed through stancetaking @xcite , in which speakers position themselves in various ways -affectively, evaluatively, epistemically, etc. -relative to a topic or a conversational partner (e.g., @xcite . sociolinguistic @xcite has studied how communities vary in their use of stance markers. stance markers are words that demarcate stance, including, among others, intensifiers (really, insanely, terribly), modals (might, should), and evaluative words (like, love, hate). for instance, compare these sentences from three reddit communities: importantly, sociolinguistic work has also argued that the contexts in which such markers appear is a critical aspect of stancetaking behavior @xcite . for example, sentences (2) and (3) use the same stance marker (incredibly), but in (3) the speaker evaluates their interlocutors in an especially negative light. sociolinguistic work posits that repeated stancetaking in a consistent way leads to stable associations of stance and identity @xcite @xcite . for example, positive evaluations like (2) reinforce the identity of r/progresspics as a supportive community, while negative evaluations like (3) contribute to the critical tenor of r/watchredditdie. this illustrates that, in addition to stance marker preferences, stance contexts are crucial for connecting stancetaking and community identity. however, while large-scale research has shown that variation in stance marker usage captures patterns of user cross-posting @xcite , to our knowledge, no large-scale work has considered how variation in stance contexts relate to community identity and cross-community engagement. here we address that gap by proposing a method for representing stance-relevant properties of a sentence that go beyond stance marker usage. specifically, our stance context representations capture higher-level linguistic properties theorized to relate to stancetaking, including affect, politeness, and formality @xcite @xcite . we focus our attention on the stance contexts in which intensifiers appear. intensifiers, like insanely and incredibly above, are a subset of stance markers that emphasize that which they modify. sociolinguistic work has shown that intensifier usage varies across linguistic and social contexts @xcite , which makes them ideal for studying community variation in stancetaking. we first show that our stance context representations reveal aspects of community identity in a manner that complements both general textual representations, as well as stance marker preference representations. we then demonstrate how similarities in stance context relate to broader inter-and intra-community engagement patterns, including user co-participation across reddit communities, and social factors of communities, including size, activity, loyalty, and density. our work thus broadens the research linking community linguistic practices to social and network properties by considering higher-level factors beyond lexical variation. future work can leverage these representations to shed light on additional aspects of platform dynamics, including community creation (e.g. are new communities sometimes created so that users may discuss the same topic but taking an alternative stance?) and user diversity (e.g. on reddit, a platform dominated by men, do the stances communities take affect the participation and engagement of women and people with a (stereotypically) feminine linguistic style?). 1 1 our code and datasets can be found at https://github. com/jaikaggarwal/emnlp_2023_stancetaking ## related work to study the relationship between language and community identity online, work in computational sociolinguistics has begun to shift from relating language choices and macrosocial categories like age, gender, and race @xcite @xcite , to a more nuanced approach to identity, adopting the framework of communities of practice @xcite . communities of practice put the focus on local identity categories that better reflect individuals' various group membership choices -e.g., presenting as a gamer, a sports fan, or an animal lover. much sociolinguistic work has explored the connection between linguistic variation and social network structure in communities of practice (e.g., @xcite @xcite . for example, @xcite noted the interrelation between how high school "jocks" and "burnouts" varied in their linguistic patterns and in the communities they interacted with. work of this kind has focused on variation within a single community or a small number of communities. online social media platforms support research relating linguistic and non-linguistic aspects of community identity at scale through analysis of multi-community settings (e.g., @xcite @xcite . for example, @xcite show positive correlations between similarities in community linguistic practices and usercommunity co-occurrence patterns. further work has shown that communities with more distinct words better retain users @xcite , and tend to be smaller, denser, more active, and have more local engagement @xcite . similar findings have been shown for communities with distinct word senses @xcite . we extend computational sociolinguistic work on community identity and engagement patterns by motivating and investigating variation of a different kind: variation in higher level properties of stancetaking. stancetaking is key to understanding how language variation reflects identity, because so many of a speaker's linguistic choices pertain to expression of stance (e.g., @xcite . moreover, much work has argued that, rather than linguistic forms being directly associated with social iden-tities, linguistic forms are first associated with stances, which in turn are associated with social identities (e.g., @xcite @xcite @xcite . thus, variation in higher-level properties of stancetaking needs to be given more attention in analyses of language and community identity. yet there has been limited computational work on stancetaking. @xcite studied variation in use of stance markers across online communities, and their word-based approach allowed them to study stance at scale. @xcite developed a more nuanced approach for representing stance, drawing on the framework from du @xcite ; however, the approach depended heavily on manual annotation, limiting analysis to a relatively small number of online communities. in our work, by automatically assessing stancerelevant properties of contexts, we are able to maintain the scale of analysis from @xcite , while getting closer to the richness of representation from @xcite . ## representing stance contexts we study stancetaking -and stance contexts in particular -on reddit, due to its nature as a multicommunity platform with free-form commenting and rich engagement patterns among its communities (subreddits). we take intensifiers -a kind of stance marker -and the stance contexts in which they appear as our testbed. intensifiers, like insanely and incredibly in (1)-(3) above, are adverbs that give emphasis to what they modify. intensifiers are an ideal domain for studying community variation. extensive empirical work in sociolinguistics has established that intensifiers vary based on social factors (e.g., @xcite ; moreover, they also exhibit much versatility in their contexts and undergo frequent change (e.g., @xcite . while past computational work has studied semantic change in intensifiers over time @xcite , to our knowledge computational work has not examined community variation in intensifiers, and their context of use, as we do here. furthermore, intensifiers are a practical choice: stance markers cover a range of grammatical classes (verbs, adjectives, interjections, and others), and focusing on intensifiers (which are adverbs), ensures that the variation we find in stance contexts is not due to variation in the grammatical contexts in which the markers appear. here we describe how we identify a set of intensifiers and extract their contexts from a large set of communities (subreddits). we then explain how we represent these contexts with linguistic features relevant to stancetaking, presenting the features, how we calculate them, and how we use them to form a vector-based representation of each community in our dataset. ## additional representations/methods here we present two other community representations to which we will compare our stance context representation: one that captures community preferences for the stance markers, and the other simple overall textual similarity. we also show how we calculate community similarity using the three representations. finally, some analyses look at groups of subreddits by topic, which are described here. ## comparing the three representations we show how the 3 kinds of community representations (textual, stance context, and marker preference) capture distinct facets of community identity. we find that they do not correlate highly with each other; moreover, they can reveal subtle differences between pairs of seemingly similar subreddits. ## stance and engagement behavior our analysis in the preceding section has established that stance context is distinct from both textual similarity and stance marker preference, and has shown preliminary evidence that these differences allow us to capture high-level properties of communities that relate to social identity. in this section, we demonstrate how the two aspects of stancetaking, stance context and stance marker preferences, relate to community engagement patterns on reddit. we first examine how well they predict subreddit cross-posting patterns. we then explore how community distinctiveness in both average stance context and stance marker preferences relates to community structure. these findings illustrate the explanatory power offered by stancebased representations, particularly stance context representations, when investigating inter-and intracommunity dynamics online. ## conclusion we extend work on community variation in stancetaking by constructing stance context representations using theoretically-motivated linguistic properties of stance. since stancetaking plays a key role in linguistic variation @xcite , considering these higher level properties of stance may help shed light on why community members present themselves in particular ways. we showed that our stance context representations capture aspects of community linguistic identity distinct from existing methods. furthermore, we found that these representations were related to inter-and intracommunity engagement patterns. our stance contexts approach also allowed us to reveal "mega" communities: those like sports and video games that share stancetaking properties while having numerous fine-grained subcommunities within them. further work with stance contexts may help shed additional light on the nuances of mega community structure: in addition to establishing communities for different subtopics (such as different video games), are "spin-off" communities (cf. @xcite sometimes created so that users may discuss the same topic while taking an alternative stance (e.g. r/pokemontrades vs r/casualpokemontrades)? additionally, by showing that higher level properties of stance relate to community identity and engagement patterns, we pave the way for much richer models of the reasons that people are attracted to, or discouraged from, online participation in various groups. for example, on reddit, a platform dominated by men, high-arousal stances are the most common due to the overrepresentation of sports and video games communities. future work could explore whether other kinds of stances (e.g. more polite or higher valence stances) lead to increased participation and engagement from women and people with a (stereotypically) feminine linguistic style.