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In perceptual dialectology, mental mapping is a popular tool used for eliciting attitudes and the spatial imprint of linguistic cognition from non-linguists, through tasking them with drawing about linguistic variations on maps. Despite the popularity of this method, research on the geometrical parameters of the shapes drawn on these maps has been limited. In our study, we utilized 500 mental maps, both digital and hand-drawn, introducing a new digital implementation for mental mapping (source code available). Our contribution presents the first perceptual dialectological outcomes of the ‘Swiss German Dialects in Time and Space’ project, which recorded a socio-demographically balanced corpus containing a large amount of quantitative personal data about participants that represent the entire Swiss German dialect continuum. Our first research question explores how various sociolinguistic variables and other variables related to personal background influence the geometrical parameters of shapes drawn, such as the number of shapes, their coverage of the language area, and their compactness. Statistical modelling reveals that dialect identity plays the most important role, while educational background, urbanity, and regional differences also affect more parameters. The second research question investigates the comparability between hand-drawn and digital mental maps, showing that they are generally comparable in terms of geometrical aspects, with minor limitations due to specific technical considerations in our digital method.

In this paper, we present the English Dialects App (EDA) and the English Dialects App Corpus (EDAC). EDA is a free iOS and Android app, launched in January 2016 that features a dialect quiz and dialect recordings. For the quiz, users indicate which variants of 26 words they use and the application guesses their local dialect; for the recordings, users can record a short text. The result is EDAC which includes metadata on mobility, ethnicity, age, educational level, and gender. More than 47,000 users from across the UK have indicated dialect variants for these 26 words, and more than 3,500 users have provided audio recordings. Unavoidably, EDAC does not successfully reflect distributions of age, ethnicity, qualification levels, and other parameters found for the UK population given that smartphone-based research reaches a specific stratum of the population. Yet there are also clear benefits to the sampling strategy used – benefits and pitfalls are discussed in this article. Future analyses will provide the most comprehensive understanding of English regional dialect variation since the work of the traditional dialectologists. We showcase two such analyses in this article. EDAC should, we demonstrate, be of interest to researchers in dialectology but also in forensic phonetics.