A Novel iOS Data Donation Approach: Automatic Processing, Compliance, and Reactivity in a Longitudinal Study

Abstract

For social scientists who are interested in understanding smartphone-usage patterns, as well as the causes and effects of smartphone use, it is crucial to get accurate estimates of smartphone and app usage. For this reason, we present a novel data donation approach based on screen video recordings of information provided by the iOS Battery Section. In this paper, we describe the Python script that can be used to process the data automatically. Moreover, we tested accuracy of the script, feasibility of the data collection method, as well as compliance and reactivity of the approach among a sample of 93 University students who participated in a 7-day mobile diary study. The findings of the study show that the approach works well, is very feasible and that the script resulted in accurate smartphone use data. Moreover, findings show that compliance rates were rather high, and no substantial signs of reactivity to the method occurred (i.e., participants did not change their smartphone behavior substantially while participating in this study). We therefore hope that the presented approach will encourage future researchers to use this or similar approaches to gain accurate smartphone use data in an ethical way.

Determinants of willingness to donate data from social media platforms

“…Participating in a data donation study is an active task that requires a basic level of digital literacy (Baumgartner, 2022), and therefore can place more burden on people with lower technical skills. Self-assessed smartphone skills in earlier studies, however, show contradictory results: Wenz et al (2019) and Ohme et al (2021)found that participants with higher phone skills were more willing to participate, but in the study of Keusch et al (2019) and Struminskaya et al (2020) these skills were not associated with the willingness to participate.…”

“…Nevertheless, data donation is more burdensome compared to other, passive modes of data sharing (Keusch et al, 2019;Silber et al, 2022), since participants are required to actively download their data or even install a software for processing purposes, and then share it with the researchers. While data donation holds great promise to study online human behavior and some recent studies reported promising results (Baumgartner et al, 2022;Breuer et al, 2022;Kmetty & Bozsonyi, 2022;Kmetty & Németh, 2022), little is known about how to best optimize such approaches.…”

Anything Goes? Youth, News, and Democratic Engagement in the Roaring 2020s

“…Fourth, we suggest using digital methods to untangle the ever-tighter nexus of communication and participation among young citizens. Digital methods based on longitudinal, high frequency measurements (e.g., Beyens et al 2021), data donations (e.g., Baumgartner et al 2022), or digital trace data (Mangold et al 2021) may offer great starting points to understand young generations’ norms, attitudes, and behaviour. Yet, such methods are mostly used in fields other than political youth studies, such as entertainment or well-being.…”

Quantifying the Systematic Bias in the Accessibility and Inaccessibility of Web Scraping Content from URL-Logged Web-Browsing Digital Trace Data

“…The proliferation of digital trace data (Baumgartner et al, 2022;Choi, 2020;Jungherr et al, 2017;Kreuter et al, 2020;Revilla et al, 2017) has led to a "Big Data" revolution (Chen & Quan-Haase, 2020;Christ et al, 2021;Eck et al, 2021;Gil de Zuniga & Diehl, 2017;Wells & Thorson, 2017). Now, social scientists can explore new questions in human behavior that were difficult or impossible to study in the past.…”