Linguistic style is central to how texts convey meaning and to tasks from register analysis to authorship verification, yet extracting detailed stylistic features at scale is hard: rule-based taggers are interpretable but slow, while neural style embeddings are fast but opaque. We present Neurobiber, a RoBERTa tagger that predicts 96 interpretable features from Biber’s Multidimensional Analysis in a single forward pass, bringing this analysis to corpus scale: a one-billion-token corpus tags on a single GPU in about 9.5 hours, over an order of magnitude faster than single-thread taggers. Neurobiber is trained on BiberPlus, a new open-source rule tagger that computes the same 96 features deterministically from explicit rules and ships built-in analytics such as PCA and factor analysis; users tag at scale with Neurobiber and fall back to BiberPlus for exact, auditable counts. Neurobiber reproduces BiberPlus at 0.97 macro-F1; its features recover Biber’s primary dimension of variation, and on PAN 2020 authorship verification they contribute signal complementary to a strong neural encoder. Both tools are released openly, with a live demo.

Email is a vital conduit for human communication across businesses, organizations, and broader societal contexts. In this study, we aim to model the intents, expectations, and responsiveness in email exchanges. To this end, we release SIZZLER, a new dataset containing 1800 emails annotated with nuanced types of intents and expectations. We benchmark models ranging from feature-based logistic regression to zero-shot prompting of large language models. Leveraging the predictive model for intent, expectations, and 14 other features, we analyze 11.3M emails from GMANE to study how linguistic and social factors influence the conversational dynamics in email exchanges. Through our causal analysis, we find that the email response rates are influenced by social status, argumentation, and in certain limited contexts, the strength of social connection.

Linguistic style matching (LSM) in conversations can be reflective of several aspects of social influence such as power or persuasion. However, how LSM relates to the outcomes of online communication on platforms such as Reddit is an unknown question. In this study, we analyze a large corpus of two-party conversation threads in Reddit where we identify all occurrences of LSM using two types of style: the use of function words and formality. Using this framework, we examine how levels of LSM differ in conversations depending on several social factors within Reddit: post and subreddit features, conversation depth, user tenure, and the controversiality of a comment. Finally, we measure the change of LSM following loss of status after community banning. Our findings reveal the interplay of LSM in Reddit conversations with several community metrics, suggesting the importance of understanding conversation engagement when understanding community dynamics.

Reddit is home to a broad spectrum of political activity, and users signal their political affiliations in multiple ways—from self-declarations to community participation. Frequently, computational studies have treated political users as a single bloc, both in developing models to infer political leaning and in studying political behavior. Here, we test this assumption of political users and show that commonly-used political-inference models do not generalize, indicating heterogeneous types of political users. The models remain imprecise at best for most users, regardless of which sources of data or methods are used. Across a 14-year longitudinal analysis, we demonstrate that the choice in definition of a political user has significant implications for behavioral analysis. Controlling for multiple factors, political users are more toxic on the platform and inter-party interactions are even more toxic—but not all political users behave this way. Last, we identify a subset of political users who repeatedly flip affiliations, showing that these users are the most controversial of all, acting as provocateurs by more frequently bringing up politics, and are more likely to be banned, suspended, or deleted.