Distinguishing In-Groups and Onlookers by Language Use (2022)

Abstract: Inferring group membership of social media users is of high interest in many domains. Group membership is typically inferred via network interactions with other members, or by the usage of in-group language. However, network information is incomplete when users or groups move between platforms, and in-group keywords lose significance as public discussion about a group increases. Similarly, using keywords to filter content and users can fail to distinguish between the various groups that discuss a topic—perhaps confounding research on public opinion and narrative trends. We present a classifier intended to distinguish members of groups from users discussing a group based on contextual usage of keywords. We demonstrate the classifier on a sample of community pairs from Reddit and focus on results related to the COVID-19 pandemic.

## introduction online communities today have unprecedented power to impact the course of disease spread @xcite , sway elections @xcite , and manipulate global markets @xcite . however, studies of online communities are often limited to single platforms due, in part, to the * these authors contributed equally to this work fact that the overlap in users across platforms is never explicitly known or because user networks and user behavior may differ across platforms @xcite @xcite . nevertheless, there are some exceptions (inter alia @xcite @xcite ) and account mapping is an area of active research (inter alia @xcite ). a powerful alternative to account mapping is to track language rather than users, which only requires data on the content of the platform and not necessarily their user base. there remain important caveats to this approach, however: 1) shifts in language can be hard to differentiate from shifts in user demographics and 2) language about a group of interest can look very similar to the language of the group itself. this is especially true if in-group vocabulary is used by outsiders when discussing the group, or if the in-group's vocabulary percolates into the general lexicon. an example of such language spread involves the word "incel", which was popularized in a specific online community before becoming more widely known. here, we address the second problem of distinguishing in-group members from onlookers engaged in discussion about the in-group, based on language alone. we introduce a group-classifier, which labels users as being in a group or discussing a group. we train our classifier on reddit, an online forum broken into explicit sub-communities (i.e., "subreddits"). we identify pairs of subreddits, where one subreddit focuses on a particular topic (e.g., covid conspiracies), and a second subreddit of "onlookers" discusses the first community or topic. consistent user participation in a subreddit implies group membership, providing training labels; we filter outlier users who participate in or "troll" their chosen subreddit's counterpart. our classifier attempts to distinguish users from each community based on their usage of topic words. our contributions in this piece are focused on two main points: the rest of this manuscript is organized as follows: in section 2 we provide an overview of prior work, mainly in the complimentary spaces of stance detection and counter speech. in section 3 we outline our methods, including the collection of a novel dataset of subreddit pairs. in section 4 we present the results from our in-group and onlooker classifier along with the impact of automatic labelling on resulting language distributions. we discuss the implications of our work in section 5 and concluding remarks in section 6. finally, in section 7 we suggest areas for future work which could build upon our in-group and onlooker framing, improve our classification pipeline, and address broader research questions. ## previous work we classify authors as being "in a group", or "discussing a group", not necessarily in an adversarial way. this closely resembles stance detection @xcite . research involving stance detection may be divided into two main categories ( alkhalifa and zubiaga, 2021): 1. predicting the likelihood of a rumor being true (i.e., rumor detection) by examining whether the stance of posts is supporting, refuting, commenting on, or questioning the rumor @xcite @xcite . in some cases, manually labelled datasets are used to evaluate the quality of stance detection pipelines @xcite or train stance classifiers using supervised learning @xcite . similar to the latter category of stance detection, topic-dependent argument classification in argument mining also parallels our classification scheme, as it may work to evaluate whether a sentence argues for a topic, argues against a topic, or is not an argument @xcite @xcite . our work is similar to the form of stance detection that evaluates "pro", "anti", or "neither" attitudes, but the problems of stance detection tend to assume that any discussion about a group are adversarial. however, the problem of distinguishing the language about a group from language of the group is much more general, as people discussing an emerging subculture do not necessarily oppose it. for example, onlookers may talk about nonpolitical groups formed around new music scenes, small social movements or communities surrounding specific activities without holding opposing views to these groups. political or not, identifying these onlookers can be of critical importance when studying a specific subculture. ## discussion the work outlined here is motivated by the challenge of accurately classifying communities that discuss the same topics but are distinct in their exact views. further, we are motivated by the task of identifying these communities in the absence of interaction data that may allow for the construction of a social graph. our methodology addresses the challenge of analyzing online conversation around contentious topics where there may be polarized communities that share similar linguistic features. for instance, when studying online discourse around a specific topic one approach to collecting relevant content is anchor wording (selecting posts based on the presence of key words defined by a researcher). in the case of r/nonewnormal and r/covidiots, "vaccine", "mask", and "covid" share similar rank values in the 1-gram distributions for each @xcite . a naive anchor-word selection would capture much of the conversation in each of these communities. however, anchor word selection would fail to disambiguate the dramatically differing views held by the majority of users in each community. this has impacts on down stream analysis such as sentiment analysis, tracking narrative diffusion, and topic modelling. considering our main motivation was a problem description and initial demonstration of a classification pipeline, we did not extensively explore model architectures or hyperparameters. we included n-gram order in the initial hyperparameter sweep when developing the logistic-regression pipeline, and results suggested that 1-grams were most effective. however, including higher order n-grams is still worth exploring more in-depth, and may have benefits for model interpretabilility and down stream results (e.g., feature importance). further, we selected the word-embedding model (the longformer) based mainly on considerations related to maximum sequence length and preliminary performance observations. additional wordembedding models could be considered-choosing models trained on more recent and/or domain specific data may be especially helpful. as in stance detection @xcite , there are several limitations to the methodology we present. first, our data set covers a limited time frame, and past work has demonstrated that models which are trained on old data sets may perform relatively poorly when fed new data @xcite . additionally, our methodology does not account for the fact that users may change opinions throughout time. for example, a user may initially be a member of a group, but a shift in opinion may cause the user to leave the group but still engage in discussion about said group. lastly, our classifier is only trained on english posts, and we cannot guarantee the same level of performance across languages. figure 3 : likelihood of correctly labeling users in in-group subreddits by user attributes. from left to right, correct labeling versus user comments in the subreddit, correct labeling versus total karma in the subreddit, and correct labeling versus mean karma in the subreddit. in all cases, the classifier performed poorly with low-activity users, better with moderate activity. we have pruned the 10% of users with the highest attributes from this plot, to improve legibility. an unabridged version of the plot is in the appendix, with a more detailed explanation. plots include only users that commented in the primary "of" subreddit. results from base-lr classifier. ## conclusion in the present study, we frame the research challenge of classifying in-groups and onlookers based on the linguistic features of social media posts. the classification task is made difficult by the significant intersection of terms shared between the two communities, which may confound classification attempts. we collect a data set of seven (7) subreddit pairs that match the in-group and onlooker-group criteria, focusing our efforts on a case study of pro-and anti-covid mitigation communities. these subreddits provide an appealing proving ground for group identification tasks, because subreddit participation acts as a noisy label in lieu of ground truth for group identity. we identify salient 1-grams that differentiate each communities' language distributions. using the full collection of subreddit pairs, we train two classifiers to assign users to communities based on their posts. we demonstrate the feasibility of the classias a divergence-of-divergences measurement, rtd 2 , shows disagreement between the divergence results derived from 1-gram distributions of generated with ground truth labels and the distribution generated with our classification pipeline. highly ranked rtd 2 values highlight the 1-grams that have the greatest difference in rank of contribution to the divergence results for each pairing. for instance, "trump" is the 1-gram with the 3 rd highest contribution in groundtruth data, whereas the 1-gram is ranked 8 th in the classifier-generated data. we stemmed the 1-grams prior to calculation of divergence results. in the case of the covid subreddits, the true and classifier-generated distributions are qualitatively similar, identifying no we hope the research questions and combined set of results is motivating for future work that leverages training generalizable classifiers on labelled community data that can then be used in a variety of settings.