The discipline of sociology focuses on interactions and group processes from the perspective of emergent phenomena at the social level. Concepts like social embedding, norms, group-level motivation, or status hierarchies can only be defined and conceptualized in contexts in which individuals are involved in social interaction. Such concepts share the property of being social facts that cannot be changed by individual intention alone and that require some element of individual adjustment to the socially given condition. Sociologists study the embeddedness of individual motivations or preferences in the context of social phenomena as such and the impact of these phenomena on individual adaptation. However, these phenomena can only be observed in individual human behavior, and this tension between the substantive focus on the aggregate level and the analytical focus on the individual level is the challenge that sociological experiments confront.

This article extends ELF studies towards variationist and computational sociolinguistics. It uses social network theory to explore how ELF is embedded in the social structures in which it is used, and it explores the size and nature of social networks in ELF. The empirical part investigates whether multilingual and often mobile ELF users have larger networks and more weak ties than others, and if they therefore could be more likely to act as innovators or early adopters of change than the other speaker groups. Our empirical material consists of real-time social media data from Twitter. The results show that, statistically speaking, social embedding of ELF creates conditions that favour change. ELF users have larger networks and more weak ties than the other groups examined here. With regard to methods, social embedding needs to be taken into account in future studies, and we illustrate that variationist and computational sociolinguistics offers a useful theoretical and methodological toolbox for this task.