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App Stores and Their Bias: Repurposing ‘App Relatedness’?

Team Members

Anne Helmond, Fernando van der Vlist, Esther Weltevrede, Carolin Gerlitz, Shefali Bharati, Taylor Geiger, Ine van Zeeland, Maggie MacDonald, Stephanie de Smale, Emanuela Blaiotta, Maria Fernanda Ibañez Duarte, Julia Wolny, Janna Joceli Omena, Ana Pop Stefanija, Christina Meyenburg, Michael Dieter, Jason Chao, Nate Tkacz, Serena Del Nero, Marco Mezzadra

Contents

Background

Mobile apps have become a popular new cultural medium. Today, the main entry point to these apps – for developers and for users alike – is via one of the app stores, where users can search for the name of individual apps (e.g. [Moodpath]) or put query to demarcate collections and genres of apps (e.g. [depression]). However, little is known about the ways in which these search results are constructed or how related app recommendations emerge. What is app relatedness within the specific setting of app stores? At first glance, related apps seem to have a topical coherence with the source app. Upon closer inspection we can ask: why or when is a mindfulness app, for example, related to a depression app? And how do larger mindfulness app spheres relate to depression app spheres? What agencies produce relations among apps (e.g., ‘You might also like’, ‘Similar apps’), and how might these algorithmic devices be repurposed? What kinds of recommendations do app stores suggest for controversial apps or apps with sensitive content (e.g., abortion apps, religion apps)? For example, when do app stores recommend bible verses or a ‘starter-kit with pro-life knowledge’ when searching for [abortion] – do app stores have a ‘pro-life bias’ (Devaney, 2016)? So more broadly, how might researchers reverse-engineer app store results and recommendations, and to what extent might researchers repurpose them for social and/or medium research?

Research Questions

  • How to reverse-engineer app store results for social and/or medium research?
  • How to reverse-engineer (the bias of) app store recommender algorithms?
  • How to compare the classification and recommendations of apps in Google Play (Alphabet) and iTunes Store (Apple)?
  • How is bias in app stores addressed socially? How do the media, journalists, developers, and others discuss the bias of app stores differently?

Subprojects

Part I: ‘There’s an app for that’

Part II: There’s no app for that

Part III: Crappy stores

References

Slides

Topic revision: r7 - 14 Jul 2018, FernandoVanDerVlist
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