Where do the young Swiss still speak grandma’s dialect?

Category: Best data-driven reporting (small and large newsrooms)

Country/area: Switzerland

Organisation: Neue Zürcher Zeitung; www.nzz.ch

Organisation size: Big

Publication date: 18/01/2019

Credit: Marie-José Kolly, Alexandra Kohler, Stefanie Hasler, Anna Wiederkehr

Project description:

We addressed dialect change through data analysis and reporter work.
We analyzed crowdsourced data: Swiss German speakers had submitted pronunciation, age, gender and local dialect. We picked extrema: Fribourg, the place with most linguistic change where the youngest generation speaks most differently from the oldest generation. And the place with the least linguistic change: Baar.
In both places we visited a family and discussed their dialect.
We tell the story with graphics showing how much (Fribourg) or how little (Baar) the linguistic landscape has changed between the oldest and youngest generation, and through dialogues between grandchild, mother and grandmother.

Impact reached:

The story addresses a wide audience: Dialects are the prestige linguistic variety in Switzerland, as opposed to Standard German; the latter is mostly used in written or in more formal oral contexts. Furthermore, German-speaking Switzerland exhibits strong variation in a relatively confined area. Therefore, the Swiss typically show a strong interest for dialect variation and change.

We measured success using several metrics such as article views and engaged time, as well as the resonance of the article in social media and the real world. The article had a wide audience on our news site (www.nzz.ch), as measured by our analytics tools, as well as on social media. A high number of spontaneous, personal messages from the general public as well as from experts in the academic field of linguistics showed us that it conveyed the topic equally well to experts and to the general public.

Techniques/technologies used:

Our methods of data analysis combine novel data, analytical methods for analyzing linguistc change as well as different statistical methods for measuring variation in categorical variables. 

The project is based on a dataset crowdsourced by a mobile app that predicts people’s dialect based on how they pronounce a set of words. This work was done by a group of Swiss researchers, one of which is the first author of the present article. This method for crowdsourcing linguistic data was, at the time (2013), novel, and a dataset of this size is still an exception in linguistic research. We fetched the data from a database via SQL code and analyzed it in R. For data analysis, we mainly used custom code that we wrote in R, along with methods used in linguistic research.

The basis for the visualizations was created in R using the library ‘ggplot2’. These plots were then processed further in Adobe Illustrator.

The dialogues in the story can be read in Swiss German as well as in Standard German. The interactive tool that allows users to switch from one variety to the other was built by the Editorial Tech Team of the Neue Zürcher Zeitung.

The final article was adapted for wide screens, for mobile screens (the graphics, in particular), as well as for the printed newspaper (parts of the story, particularly around the graphics, were rewritten).

What was the hardest part of this project?

The hardest part was the fact that the data analysis showed many interesting micro-level patterns (strong linguistic change in some places, almost none in others) buth that linguistic change did not correlate with any of the variables that we had tested (e.g. urbanization, population density, medium age, …). We therefore used the data analysis to pick «pars pro toto» places and explain dialect change and the reasons for it with a much more personal note than first planned. 

What can others learn from this project?

We believe that the story is innovative: It revolves around people and their language, it features dialogues and video snippets. It is data journalism in which the graphics do not take the center stage, yet the story is completely data-driven. 

We like this approach where data journalism is about much more than data and graphics. Also, we think that this is a good example of how one can turn a story around and publish something completely different from what was pitched (but that turns out to be, in this case, better than what was pitched). 

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