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Review of 'Crowdsourcing Language Change with Smartphone Applications'

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What are the benefits and drawbacks of using a smartphone app to crowdsource language change data?
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    Rated 4 of 5.
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    Rated 4 of 5.
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Crowdsourcing Language Change with Smartphone Applications

Crowdsourcing linguistic phenomena with smartphone applications is relatively new. In linguistics, apps have predominantly been developed to create pronunciation dictionaries, to train acoustic models, and to archive endangered languages. This paper presents the first account of how apps can be used to collect data suitable for documenting language change: we created an app, Dialäkt Äpp (DÄ), which predicts users’ dialects. For 16 linguistic variables, users select a dialectal variant from a drop-down menu. DÄ then geographically locates the user’s dialect by suggesting a list of communes where dialect variants most similar to their choices are used. Underlying this prediction are 16 maps from the historical Linguistic Atlas of German-speaking Switzerland, which documents the linguistic situation around 1950. Where users disagree with the prediction, they can indicate what they consider to be their dialect’s location. With this information, the 16 variables can be assessed for language change. Thanks to the playfulness of its functionality, DÄ has reached many users; our linguistic analyses are based on data from nearly 60,000 speakers. Results reveal a relative stability for phonetic variables, while lexical and morphological variables seem more prone to change. Crowdsourcing large amounts of dialect data with smartphone apps has the potential to complement existing data collection techniques and to provide evidence that traditional methods cannot, with normal resources, hope to gather. Nonetheless, it is important to emphasize a range of methodological caveats, including sparse knowledge of users’ linguistic backgrounds (users only indicate age, sex) and users’ self-declaration of their dialect. These are discussed and evaluated in detail here. Findings remain intriguing nevertheless: as a means of quality control, we report that traditional dialectological methods have revealed trends similar to those found by the app. This underlines the validity of the crowdsourcing method. We are presently extending DÄ architecture to other languages.
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    Review text

    This project is an interesting one and provides a step into the logical next step of studying language change. Using crowdsourcing via a mobile app available for iOS, the authors collected age and location data for Swiss German speakers and also collected their use of different variables. These data were compared to a 70-year-old dialectological survey of Swiss German to investigate language change. 

    Most of this paper focuses on methodology and considerations, which is good. The fact that they received the highest rate of correct identification for the oldest speakers falls in line with other studies that look at language change by the age of their subjects. The 70 year old respondents, logically, had dialect placements the most similar to the 70 year old dialect maps. It is very interesting that the rates of correct placement decrease as age decreses, which is an indicator of language change. The authors perform some analysis on responses to three variables, but (helpfully) avoid stating that this is definitive proof of language change. They use these data to corroborate another study of /l/-vocalization which found similar patterns. 

    It is tempting, as a scholar, to want to use these kinds of technologies to obtain all of the data possible; however, it is important, as these authors point out, to maintain a balanced worldview and keep in mind the drawbacks of the platform and how it might affect the data. The authors do a good job of explaining these drawbacks. 

    These data and this methodology can be complementary to other dialect studies. They can be done first to identify potential interesting areas of language change which could then be investigated more qualitatively. In this instance, the data seem to suggest that there are some areas of Swiss German that are changing rapidly and may be a good in-depth project for a dialect researcher. This method could also be done after dialect studies have been done, as the authors did with /l/-vocalization in this study, to corroborate findings with quantitative data. However, though the authors do not directly state this, I do not think this kind of method could exist on its own for dialect study; there are simply too many confounding variables involved. This is a useful tool for the language researcher to have in their toolbox, and I look forward to further developments of the platform and results from other dialect areas. I am particularly curious about its use for English-speakers, because (as the authors point out) English speakers tend to be unaware of some parts of their dialect. In the US, where I am, there are areas with low level indexicality, that is where speakers of local dialects are not aware that they even speak differently from others. I wonder if the app would pick up on this at all, or if these speakers would simply input something close to the standard because they think that they speak Standard American English. Perhaps this could be used for a future application of this method. 

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