We analyze Nordic social media users by clustering them based on their connections on Twitter. The data consists of 15,794 users in the five Nordic countries: Finland, Sweden, Norway, Denmark, and Iceland. We first create an undirected graph from the friendship relations (mutually following each other), then divide the graph into five clusters using a recent M-algorithm, and finally compare the results to users’ locations. The results demonstrate that the users are strongly clustered according to their home country. There is surprisingly little interaction across the countries despite the fact that they are, except for Iceland, physically close to each other and have cultural and linguistic similarities. The main language of the four countries belongs to the Germanic languages, while Finnish is typologically distinct. We further explore content from users in each country, analyzing its alignment with connectivity patterns. Our findings reveal a discrepancy between user-generated content similarity in the Nordic region and their connectivity patterns.

We investigate the uses of fuck in digital social networks from social media, Twitter/X in this case. Social media outlets have so far been predominantly treated as massive text collections, but they can be effectively used to investigate the role of social networks in shaping human communication. We use user-generated texts from 5,660 social networks (with 435,345 users and 7.8 billion words) from three settings (UK, US, and Australia). With embedded network information, this massive dataset enables us to investigate how network properties, that of the size and the strength of the network, influence the use of offensive words in these three settings. Our findings show that Americans use fuck most frequently, while Australians least frequently but they are highly creative with spelling variants of the word. Contrary to prior studies, we observe that people on this social media application swear more with acquaintances than with friends, but only in smaller networks − in larger networks of >100 people, the differences level out. Overall, this study highlights the benefits of using social media data that can be enriched to allow access to the social networks that people interact in.

Plural verbal agreement with sports teams (“Liverpool have beaten Manchester United 4-0 tonight”) is a distinct feature of British English. Prior corpus-based studies show that plural verbal agreement tends to be more frequent with collective nouns in sport texts than in other news (Hundt 1998; Levin 2001). However, beyond these genre-level differences, there are significant gaps in our understanding, particularly regarding sociolinguistic and individual-level variation in this grammatical domain.

This presentation investigates the verbal agreement patterns of individuals who use English as a second language (L2) in public settings. Our study focuses on foreign-born football managers working in England, specifically in the English Premier League, which has attracted managers from various European countries since the late 1990s. These managers, who are public figures and native speakers of languages other than English, conduct pre- and post-game press conferences in English as part of their managerial duties. The individual informants were chosen because the agreement patterns in their native languages favor singular verbal agreement.

We are interested in whether our these L2 speakers of English use singular or plural verbal agreement when talking about their own teams and their opponents. Additionally, we aim to determine if these agreement forms remain stable or change during their careers in England.

To explore this phenomenon, we utilize an underused source for accessing individual variation: a large set of interviews and press conferences available online. We use automatically-generated transcripts from YouTube recordings, with data obtained through national digital humanities infrastructure projects in Finland and in Sweden. This dataset includes recordings of managers’ interviews and press conferences spanning roughly a decade from the mid-2010s onwards.

The topic is of interest to various audiences, not limited to those studying agreement patterns. Our unique data collection also appeals to people interested in digital humanities. The corpus-based observations not only shed light on verbal agreement patterns in proper noun + verb structures but also contribute to debates on how idiolects change in the short-term post-adolescence (Sankoff 2018). The results could provide insights into how non-native adult speakers handle stable lexico-grammatical changes in society. Prior studies have identified three trajectories of individual change: stability, adoption, and swimming against the community current.

The use of very large social media datasets in corpus linguistics has obviousbenefits. Such data represent a novel source of evidence when compared with structured digital text corpora. However, there is a clear need to assess critically how the effective reuse of data can be handled, how findings can be reproduced, and how results can be generalized. A relevant question concerns the presentation of data to ensure reproducibility and replicability. This article surveys the state-of-the-art of descriptions of data collection and methodological transparency in 30 studies that used Twitter/X as their data. The empirical section investigates how easy it would be to reproduce a study based on these descriptions. While we concentrate on evidence from one social media application, the discussion continues to a presentation of concrete steps that might be used to improve data management related to the reuse, discovery, and evaluation of social media data in general.

This article combines the study of large-scale social media data with social network theory in sociolinguistics. Given that the purpose of social media is to form networks and communities, big data from social media applications could have substantial potential in deepening the understanding of the role of networks in language variation and change. The study first presents an algorithmic method suitable for directed-graph ego networks in computer-mediated communication. This method measures network strength and enables us to enrich social media data with a network parameter that indexes how strongly (or loosely) people in a network are connected to each other. We then use a large dataset of c. 4.8 billion words from nearly four thousand networks to study how network strength conditions linguistic change. The results show that online networks are highly similar to traditional offline networks, a finding that enables fixing a major methodological limitation in the study of weak ties, namely that the method is less suited for studying socially and geographically mobile individuals. This finding makes it possible to apply the theory of social networks in sociolinguistics to very large digital networks in social media.

This article looks into using large-scale social media data in SSH research and in particular in studies of language variation and change. It presents a study that investigates the role of social networks in linguistic variability. Previous studies have convincingly shown that networks in which people are connected to each other in loose ties tend to contribute positively to language change. Conversely, networks in which people are closely connected to each other inhibit change. This conclusion is, however, based on small datasets from small networks, and this study tests if the difference is diluted when network size is closer to human average. The results from 3,935 networks suggest this to be the case. Towards the end, the article suggests numerous ways in which large-scale social media data and the use of data intensive methodologies could be increased and encouraged in SSH research.

Social media data have substantially enlarged the potential pools of evidence in the study of variation and change in English. They offer access to language use of large numbers of informants, but the downside is that they contain inadequate social background information. This seriously restricts the theoretical insight, limiting investigations to the study of actuation of change and leaving out those that focus on diffusion. It is widely held that while actuation is largely functional, diffusion requires information on the broader social structures within which speakers belong. We present an algorithmic method for adding social information to Twitter. Our method builds on interaction parameters (size and structure of networks, similarity, and communication frequency). Using such participant-centred information as a proxy for social information increases the empirical validity substantially. In addition, the article presents a case study of how networks of varying strength condition ongoing change. The data are drawn from five metropolitan centres in the UK and from English as a lingua franca setting in the Nordic region. The results suggest that network size plays an important, yet understudied, role in the social network theory in sociolinguistics.

The study of ongoing change in English typically focuses on studying evidence from codified varieties of the language. Recent corpus studies show, however, that advanced non-native users of English may display heightened sensitivity to features undergoing frequency shifts similar to that experienced by native speakers. In addition, most studies aiming to detect patterns of linguistic regularity utilize large data sets that attempt to minimize the presence of the individual. In this study, we focus on change in ELF and place non-native individuals at the center of attention. Our empirical section examines how aggregated features that are currently undergoing change in codified varieties of English vary in the repertoires of ELF users of Twitter. To carry out this task, this study utilizes geo-tagged tweets retrieved from the Nordic Tweet Stream. The data obtained from this real-time monitor corpus are freely available for research and re-use at . For the analysis itself, we selected the idiolects of 150 individual users who actively tweet in English from geographically varying locations in Finland. As American English predominates with several patterns of linguistic change in codified varieties of English, a simplified dichotomy between American and British features is utilized as a conceptual tool for inspecting variation. The idiolects are analyzed from the perspective of spelling and lexico-grammatical and morphological variation, such as V + -ing |V + infinitive (e.g. start doing | start to do) and expanded predicates (e.g. take a look | have a look). The quantitative observations show that, particularly in the case of grammatical features, ELF speakers appear to have generally adhered to ongoing linguistic change.