Apps and Their Stories: Volatility, Diversity, Policy
Anne Helmond, David Nieborg, Fernando van der Vlist, Esther Weltevrede (alphabetical)
Evelien Christiaanse, Tidiane Cisse, Michael Dieter, Mike Huntemann, Gemma Newlands, Jason Park, Oana Patrichi, Vanessa Richter, Ziwen Tang, Anna Teresova (alphabetical)
Mobile apps have become a popular new cultural medium and economic form. Today, the main entry point to these apps – for developers and for users alike – is via one of the popular app stores, where users can search for the names of individual apps (e.g. [Moodpath]) or put query to demarcate collections and genres of apps (e.g. [depression], [messaging]). However, little is known about the ways in which these search results are determined, how related app recommendations emerge, and what the implications are, especially for developers.
In this project, we engage with the technicity of competing ideas through apps in the most popular app stores, Google Play and the App Store. How do app stores modulate and regulate the visibility of certain kinds of apps? Which kinds of apps are ranked highly? How, where, and by whom is this visibility managed and controlled (cf. Bucher, 2013
; Rieder et al., 2018
)? These questions concern both algorithmic and economic power as well as their societal consequences. We will continue our ongoing, collaborative, methodological exploration of app stores as a particular kind of online platform and explore their epistemologies and research affordances (cf. Weltevrede, 2016
; Dieter et al., 2018
In collaboration with the Netherlands Authority for Consumers and Markets (ACM), we have developed a number of exploratory research questions in three areas: app ranking volatility, app diversity, and app developer conditions.
- App volatility
- How are apps ranked in Google Play and the App Store, and how have their app ranking mechanisms changed over time? What are the underlying ranking parameters? And how do these parameters differ between Google Play and the App Store?
- How can we examine app store ranking volatility (over time)?
- Do app store owners (Google and Apple) prefer their own apps in their rankings?
- App diversity
- Do app stores use similar app categories, or do they have distinctive categories?
- How do the top apps, across app store categories, compare across Google Play and the App Store? How distinctive are the top apps in these categories (e.g., in terms of topics, types, developers, popularity)? How can we characterise the distribution of apps per category?
- App store policy (and developer conditions)
- How have app stores developer policies changed over the years (‘App Store Review Guidelines’, ‘Developer Policy Center’)? How can we contextualise changes in these documents?
- How have definitions of apps changed?
- What is an ideal app to the app store owner? What are best practices?
- How have ideas of objectionable and prohibited content changed over time? When do apps ‘cross the line’?
- Part I: Volatility
- Part II: Diversity
- Part III: Policy
- Balashankar, A., Koc, L., & Guimaraes, N. (2016, December 14). App Discovery with Google Play, Part 2: Personalized Recommendations with Related Apps. Google AI Blog. Retrieved from: http://ai.googleblog.com/2016/12/app-discovery-with-google-play-part-2.html.
- Devaney, B. (2016). Does Apple have a problem with abortion? The App Store pro-life bias. Gadgette. Retrieved from: https://www.gadgette.com/2016/01/05/does-apple-have-a-problem-with-abortion-the-app-store-pro-life-bias/.
- Dieter, M., Gerlitz, C., Helmond, A., Tkacz, N., van der Vlist, F., & Weltevrede, E. (2018). Store, interface, package, connection: Methods and propositions for multi-situated app studies. CRC Media of Cooperation Working Paper Series No. 4. CRC Media of Cooperation: University of Siegen. Retrieved from https://bit.ly/wps1187-4.
- Gerlitz, C., Helmond, A., van der Vlist, F. N., & Weltevrede, E. (2016, December). App support ecologies: An empirical investigation of app–platform relations. _1st 2016 Annual Conference of the CRC Media of Cooperation. Retrieved from: http://bit.ly/app-support-ecologies.
- Haldar, M., MacMahon, M., Jha, N., et al. (2016, November 8). App Discovery with Google Play, Part 1: Understanding Topics. Google AI Blog. Retrieved from: https://research.googleblog.com/2016/11/app-discovery-with-google-play-part-1.html.
- Lee, H-C., Chen, X., & An, Q. (2017, January 30). App Discovery with Google Play, Part 3: Machine Learning to Fight Spam and Abuse at Scale. Google AI Blog. Retrieved from: https://ai.googleblog.com/2017/01/app-discovery-with-google-play-part-3.html.
- Rahman. M. (2018, June 27). Developers are facing huge drop in new installs after Play Store algorithm changes. XDA Developers. Retrieved from: https://www.xda-developers.com/developers-huge-drop-new-installs-play-store-algorithm-changes/.
- Rieder, B., Matamoros-Fernández, A, & Coromina, O. (2018). From ranking algorithms to ‘ranking cultures’: Investigating the modulation of visibility in YouTube search results. Convergence 24(1): 50–68. DOI: 10.1177/1354856517736982.
- Vonderau, P. (2018). App Annie: App Industry Rankings and Infrastructure. In: Morris JW and Murray S (eds) Appified: Culture in the Age of Apps. Ann Arbor, MI: University of Michigan Press, pp. 23–31.
- Walz, A. (2015, May 27). Deconstructing the App Store Rankings Formula with a Little Mad Science. Moz. Retrieved from: https://moz.com/blog/app-store-rankings-formula-deconstructed-in-5-mad-science-experiments.
- Weltevrede E. (2016). Google algorithm changes and the volatility of method. In: Repurposing digital methods: The research affordances of platforms and engines. Ph.D. thesis, University of Amsterdam. Retrieved from: http://hdl.handle.net/11245/1.505660.