Register Studies - Current Issue

This paper introduces an initial text typology of social media posts from a multi-dimensional (MD) perspective. Text types are “[g]roupings of text that are similar in their linguistic form” (Biber 1989: 13). This text typology is based on a new MD analysis of social media messages presented in the paper. The corpus consists of 60,000 social media messages in English compiled from Facebook, Twitter, Instagram, Reddit, Telegram, and YouTube. After the texts were cleaned up, the corpus was tagged with the Biber Tagger and post-processed with the Biber Tag Count. Three dimensions of variation were determined, each representing an underlying parameter of variation. Once the texts were scored on each of the dimensions, a k-means cluster analysis was carried out, and the optimal number of clusters was determined using the Cubic Clustering Criterion statistic. A two-way typology was developed based on the dimensional characteristics of each cluster and on careful qualitative analysis of text samples.

Linguistic expressions in social media vary along many axes, including author style, the specific medium and its affordances, and others. In this paper, we argue that different registers must be distinguished within social media and that register should be included as an important factor independent of (social) medium in analyses of variable linguistic phenomena. We introduce a new German cross-media corpus, consisting of blog posts and tweets from the same 44 authors. We define the registers as ‘Informative’, ‘Narrative’, and ‘Persuasive’, based on situational characteristics of the texts. We then correlate the registers with two variable linguistic phenomena: German modal and intensifying particles. In each case, we document considerable inter- and intraindividual variation in the expressions used and their frequency across texts. The statistical analysis shows that the register grouping corresponds more closely to linguistic similarities between texts than the grouping by medium does.

The importance of digital data in forensic contexts has been increasing continuously (e.g., Grant 2013; Layton, Watters & Dazeley 2010; Wright 2013), with individuals holding an average of 8.5 different social media accounts in 2018 (Statista 2021a). Even though numerous studies have investigated registers on social media platforms (e.g., Seargeant & Tagg 2014; Zappavigna 2013), it has rarely been attempted to describe individual styles of one and the same person on different platforms – a research gap this paper attempts to address with the help of an exploratory hypothesis-generating study. The data is drawn from Instagram and Twitter, and comprises 1,800 posts from three media representatives and/or writers that hold accounts with both platforms. The results of the analysis suggest that the use of some features (e.g., emoji, hashtags) is strongly influenced by the respective platforms, while other features (e.g., patterns of punctuation, use of types of speech acts) remain stable and thus offer promising avenues for authorship analysis.

This study analyses epistemic stance in social media climate change discussions, contributing to our understanding of how factuality and likelihood are evaluated in climate change discourse. Using a corpus of 1.2 million words, the paper compares the frequencies of epistemic stance in climate change sceptic and climate change proponent discourses on two social media platforms, Twitter and Reddit. Based on the quantitative analysis, the paper argues that both platform and climate change beliefs influence register in terms of epistemic stance. Overall, Reddit uses more epistemic stance markers than Twitter. Sceptics use less hedging of likelihood and more lexis evaluating the factuality and reliability of their opponents. The interpersonal functions of epistemic stance are shown to be associated with different platform uses and affordances and with the different goals, worldviews, and concerns of the factions. The study thus calls for further linguistic comparison of platforms and different factions within the platforms.

Similar to lexical and grammatical choices, the length of a text is also guided by situational constraints and functional needs. Consequently, texts of different lengths are associated with different communicative functions. This study explores the role of register in the functions which are associated with comment lengths on the social media platform Reddit. Since registers differ in their functional and situational makeup, the same text length may also have different functions in different registers. By analyzing variation in the frequencies of register features across comment lengths in a number of popular subreddits in a large-scale dataset of Reddit comments, I show that the functional associations of text length can differ greatly between subreddits, and that comments of the same length can even have virtually opposite functions in different subreddits. Furthermore, some subregisters are clearly differentiated not only by their feature makeup but also by the length of their comments.

Informed by the framework of register analysis (Biber & Egbert 2018) and narrative analysis (Labov & Waletzky 1967), this paper studies customer reviews of assistive reading devices to identify intra-register variation between narratives of one’s own and another’s experience. The lexico-grammatical features differentiating the two experience types include the use of pronouns, references to animate and inanimate entities, patterns of user and product description, stance markers, negation, as well as expectation- and transaction-related lemmas. The study reveals that the distribution of these features is determined by the narrative ownership and the associated distinctions in expectations. Furthermore, the difference in narrative ownership results in variation with respect to the experience narrative composition.