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Lörres, Möppes, and the Swiss. (Re)Discovering regional patterns in anonymous social media data

Published online by Cambridge University Press:  12 December 2019

Christoph Purschke  and
Dirk Hovy
Christoph Purschke*
Affiliation:
University of Luxembourg, Esch-sur-Alzette, Luxembourg
Dirk Hovy
Affiliation:
Bocconi University, Milan, Italy
*
Author for correspondence: Christoph Purschke, Email: christoph.purschke@uni.lu
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Abstract

We study regional similarities and differences in language use on an anonymous mobile chat application in the German-speaking area. We use a neural network on 2.3 million online conversations to automatically learn representations of words and cities. These linguistic-use-based representations capture regional distinctions in a high-dimensional vector space that can be clustered and visualized to discover patterns in the data. We find that the resulting regional patterns are closely linked to the traditional division of German dialects, even though most of the conversations are written in standard German. The resulting maps correspond to traditional dialect divisions and language-external spatial structures, with a few notable exceptions that can be explained through external factors.

Our method also facilitates two qualitative analyses, allowing us to discover geographically-pertinent words for various regional levels, as well as creating regional group-specific style profiles based on various linguistic resources. The results of our study strongly suggest the existence of region-specific patterns of language use (“digital regiolects”) representing distinctive strategies of linguistic stylization in relation to linguistic resources and topics. As a methodological contribution, we show how linguistic theory can drive the application and direction of neural network-based representation learning, and how their judicious application provides the basis for qualitative analysis of large-scale data collections.

Keywords

computational sociolinguisticsregional variationGermanlanguage usesocial stylesocial medianeural networksrepresentation learningword embeddingsdistributed representationsdistributional semantics
Type
Articles
Information
Journal of Linguistic Geography , Volume 7 , Issue 2 , October 2019 , pp. 113 - 134
Copyright
© Cambridge University Press 2019 

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