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.

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.

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.

‘Native-like’ use of discourse markers is a good indicator of language proficiency. Analysing four subcorpora of English-language tweets posted by Twitter users from the Nordic countries of Finland, Norway, and Sweden, this study considers the effects of discursive context and L1 influence on the correlation between semantic function and sentence position of the discourse marker actually. The study shows that both predictors appear to have a significant effect. A more formal context predicts more standard punctuation, distribution of the pragmatic functions, and placement of the discourse marker, and L1 influence is reflected in the preferred sentence position, with a substantial and significant difference observed between the Finnic and Germanic L1s. Furthermore, the study shows that while the discourse marker actually is significantly more frequent in colloquial Twitter language than in spoken English, the frequency is significantly lower and in line with spoken English in more constrained contexts.

Earlier studies have established that the (perceived) similarity of users is highly subjective and reflects more on how people respect/admire others rather than their characteristics or behavioral similarities. We study this phenomenon among Twitter users, and while confirm that it is indeed the case, we further explore the components of similarity by investigating it using data from three categories (interactions between egos and alters, profile-based activity history, and linguistic content in the messages). We use interactions as estimation for admiration and observe that it has more impact and a higher correlation to the perceived similarity than other objective measures, including similarity based on user profiles and their use of hashtags.

Investigation of social networks formed by individuals in various contexts provides numerous interesting and important challenges for researchers and practitioners in multiple disciplines. Within the field of variationist sociolinguistics, social networks are analyzed in order to reveal the patterns of language variation and change while taking the social, cultural, and geographical aspects into account. In this field, traditional approaches usually focusing on small, manually collected data sets can be complemented with computational methods and large digital data sets extracted from online social network and social media sources. However, increasing data size does not immediately lead to the qualitative improvement in the understanding of such data. In this position paper, we propose to address this issue by a joint effort combining variationist sociolinguistics and computational network analyses with information visualization and visual analytics. In order to lay the foundation for this interdisciplinary collaboration, we analyse the previous relevant work and discuss the challenges related to operationalization, processing, and exploration of such social networks and associated data. As the result, we propose a roadmap towards realization of visual sociolinguistic network analysis.

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 explores the size and nature of social networks in ELF. The empirical part investigates if 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 favor 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.