This paper presents a topic modeling approach to corpus-based dialectometry. Topic models are most often used in text mining to find latent structure in a collection of documents. They are based on the idea that frequently co-occurring words present the same underlying topic. In this study, topic models are used on interview transcriptions containing dialectal speech directly, without any annotations or preselected features. The transcriptions are modeled on complete words, on character n-grams, and after automatical segmentation. Data from three languages, Finnish, Norwegian, and Swiss German, are scrutinized. The proposed method is capable of discovering clear dialectal differences in all three datasets, while reflecting the differences between them. The method provides a significant simplification of the dialectometric workflow, simultaneously saving time and increasing objectivity. Using the method on non-normalized data could also benefit text mining, which is the traditional field of topic modeling.

This paper aims to identify and analyze geographical patterns of (morpho)syntactic variation in traditional Austrian dialects using non-aggregative dialectometric methods (factor analysis). Based on a comprehensive dialect corpus obtained by direct dialect interviews including 163 speakers from 40 locations throughout Austria, our analyses of 79 variants of 30 (morpho)syntactic variables not only show geographical patterns in Austria’s dialects, but also address the linguistic basis of the geographical structures revealed. In particular, the results show that variables at the morphology–syntax interface contribute most to geographical structuring. We argue that this finding is related to structural conditions of these variables and the historical development of the respective variants.

Maps are important in many areas of linguistics, especially dialectology, sociolinguistics, typology, and historical linguistics, including for visualizing regional patterns in the distribution of linguistic features and varieties of language. In this hands-on tutorial, we introduce map making for linguistics using R and the popular package ggplot2. We walk the reader through the process of making maps using both typological data, based on the World Atlas of Language Structures, and dialect data, based on large corpora of language data collected from German and American social media platforms. This tutorial is intended to be of use to anyone interested in making maps of linguistic data, and more widely to anyone wanting to learn about mapping in R.

This paper provides a new classification of Central–Southern Italian dialects using dialectometric methods. All varieties considered are analyzed and cast in a data set where homogeneous areas are evaluated according to a selected list of phonetic features. Using numerical evaluation of these features and the Manhattan distance, a linguistic distance rule is defined. On this basis, the classification problem is formulated as a clustering problem, and a k-means algorithm is used. Additionally, an ad-hoc rule is set to identify transitional areas, and silhouette analysis is used to select the most appropriate number of clusters. While meaningful results are obtained for each number of clusters, a nine-group classification emerges as the most appropriate. As the results suggest, this classification is less subjective, more precise, and more comprehensive than traditional ones based on selected isoglosses.

Geolectal variation is often present in settings where one language is spoken across a vast geographic area. This can be found in phonological, morphosyntactic, and lexical features. For practical reasons, it is not always possible to conduct fieldwork in every single location of interest in order to obtain the full pattern of variation, and a sample of them must be chosen. We propose and test a method for sampling these locations, with the goal of obtaining a distribution of typological features representative of the whole area. We apply k-means and hierarchical clustering algorithms for defining this sample, based on their geographic distribution. We test our methods against simulated data with several spatial configurations, and also against real data from Circassian dialects (Northwest Caucasian). Our results show an efficiency significantly higher than random sampling for detecting this variation, which makes our method profitable to fieldworkers when designing their research.

To investigate the under-researched topic of sign language vitality, the authors evaluate a set of sociolinguistic data gathered as part of the Sign Hub project. The subproject Atlas aimed to create an interactive online tool for researchers, teachers, and interpreters to compare sign languages’ sociolinguistic, grammatical, lexical, and phonological features. This paper presents an analysis of ten sign languages, i.e. the first batch of socio-historical data submitted to the subproject. The authors find that nearly all of them have been subjected to oppression, and their documentation is limited. Their vitality is supported by good awareness among the hearing community and use within educational institutions, national deaf associations and local deaf clubs. Vitality is threatened by low provision of sign language media and a lack of interpreter training. The paper concludes that the Atlas has considerable utility in research on sign language vitality, which may be augmented by adding further diachronic components.

The present study offers an examination of attitudes and perceptions of the Ukrainian language by respondents who have lived at least half of their life in Ukraine; they were asked to draw on a map of Ukraine where the most correct Ukrainian is spoken and where the Ukrainian that grates on one’s ears is spoken. Recruitment for the online survey was conducted by placing ads on several Ukrainian-language Facebook pages, along with a link to the survey. The findings presented are from a total of 90 analyzed surveys. Respondents’ maps were analyzed and compared using QGIS software. The research demonstrates that that there is a tug-of-war of correctness between Kyiv and Lviv. It also shows that there is an overall tendency of native speakers to evaluate the Transcarpathian region as the area that grates one’s ears.

This paper examines folk perceptions of language in the Greater Boston Area. In particular, it seeks to understand which areas are associated with a “Boston” accent, and whether associations are changing given recent shifts in ethnic and economic demographics. A total of 111 Greater Boston residents completed a survey and map task asking what constitutes a “Boston” accent, who has one, and in which areas one can be heard. Results show that the majority of participants perceive the neighborhood of South Boston to be the geographic epicenter of the “Boston” accent, and generally associate accents with historically White working-class areas, despite sometimes changing demographics within them. There is also evidence that participant ethnic background may play a role in perceptions of speech in some areas, with White men less likely to choose South Boston, widely viewed as gentrifying, as accented, and Black and Asian participants less likely to choose the increasingly ethnically diverse neighborhood of North Dorchester. These results demonstrate the importance of eliciting folk perceptions from residents of color to obtain a fuller picture of the language attitudes in a given community.

This study explores some consonantal and vowel features of the variety of American English spoken in Dearborn, MI. Recent research shows that Dearborners recognize their English variety as distinct. Past studies on Dearborn English have primarily focused on vowel patterns, while some emerging sociolinguistic work has paid attention to the consonantal features. The present study contributes to this emerging literature by presenting a preliminary analysis of some consonantal features of Dearborn English in addition to a description of its vowel pattern. To do so, the speech of MENA Americans in Dearborn has been compared to that of non-Dearborner MENA Americans from the US Upper Midwest. The results show that pre-vocalic word-final /t/ glottalization, convergence of voice onset time of lenis and fortis members of bilabial and velar stops, shorter Euclidean distances for vowels, and a vowel pattern not consistent with local patterns could be parts of an ethnolinguistic repertoire for Dearborners.

This study uses a modified online version of the “draw-a-map” task and Garrett, Williams, and Evans’ (2005b) “keywords” methodology to explore the geospatial distribution of different accent and dialect labels and descriptors in Greater Manchester, UK. Specifically, we consider the distribution of the three most frequent labels related to “accentedness”: Broad, Strong, and Soft, as provided by 349 Greater Manchester residents. This analysis finds that these descriptors were clustered in separate areas of Greater Manchester, suggesting that they were being used to describe perceptually distinct varieties of English. In order to uncover the nuances in these folk-linguistic terms, we consider how they correlate with other concepts emerging from the dataset, finding that they are being used to differentiate between varieties with contrasting social associations. By combining innovative approaches, this study demonstrates how the subtleties of folk-linguistic modes of awareness can be uncovered through in-depth analysis of the terminology employed to describe linguistic variation on a very local scale. In so doing, it paves the way for further development of draw-a-map techniques that will enable similarly nuanced analysis in different regions, thus pushing the sub-discipline forward.