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Data donation and linking digital trace data 2 |
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| Session Organisers | Ms Laura Boeschoten (Universiteit Utrecht) Mr Johannes Breuer (GESIS) Mr Zoltán Kmetty (Centre for Social Sciences) Mrs Júlia Koltai (Centre for Social Sciences) Mr Adam Stefkovics (Harvard University) Ms Bella Struminskaya (Universiteit Utrecht) |
| Time | Tuesday 18 July, 16:00 - 17:00 |
| Room | U6-28 |
Digital traces on digital platforms such as Facebook, Instagram, Google, Whatsapp, etc., and other online traces left by citizens are promising sources of information for scientific research in various fields. Although there are multiple ways to access digital data traces, in recent years, a new approach built on the partnership with citizens has emerged. Donated data can be obtained through installing web and app trackers on participants’ devices, or through data download packages from digital platforms. As opposed to self-reports from surveys which may suffer from measurement error due to recall or social desirability bias, digital traces can provide reliable, behavioral data free from those error sources. When combined with self-report, validity and reliability of measures derived from digital traces can be investigated. Linking several digital trace data sources can provide more insights into the phenomenon but also brings challenges.
While research is growing in this field, we still know little about how to best optimize digital donation approaches, the patterns and determinants of participation and ways to preserve participants’ privacy and linking digital trace data with survey responses.
We invite contributions for the session which provide new theoretical or empirical insights into any phase or aspect of donation of digital trace data. Contributions may cover the following topics but not limited to:
· Data donation methods and methods of data extraction
· Willingness to donate digital trace data, best practices for recruitment
· Sampling, and nonparticipation errors, missing data
· Validity of digital trace data
· Privacy issues, ethical issues, anonymization
· Issues of linking digital data with survey data
· Challenges, analyzing combined data
· Substantive contributions which combine digital trace and survey data
Keywords: linkage donation digital trace social media
Dr Zoltán Kmetty (Centre for Social Sciences) - Presenting Author
Mr Ádám Stefkovics (Centre for Social Sciences)
Dr Elisa Omodei (CEU)
Ms Júlia Számely (CEU)
Ms Deng Dongning (Eötvös Loránd University)
Dr Júlia Koltai (Centre for Social Sciences)
Social media data donation through data download packages (DDPs) is a promising new way of collecting individual-level digital trace data with informed consent. When linked with survey data, social media data donation is an even more promising tool that helps answer novel research questions. Nevertheless, given the novelty of this approach little is known about whether and how people would share their data with researchers. To study the determinants of data-sharing and help future data donation studies, we embedded two vignette experiments in two online surveys conducted in Hungary and the U.S. In hypothetical requests about donating social media via DDPs, we manipulated the amount of the monetary incentives (1), the appearance of non-monetary incentives (2), the number of platforms (3), the estimated upload/download time (4), and the type of data (5). The results revealed that data-sharing attitudes are strongly subject to the parameters of the actual study, how the request is framed (high random effects of the vignette characteristics), and some respondent characteristics. Monetary incentives increased willingness to participate in both countries, while other effects were not consistent between the two countries. For instance, non-monetary incentives and time to download/upload materials influenced willingness in the US sample but not in the Hungarian, whereas the type of data mattered only for the Hungarian respondents. Our findings help construct more effective future data donation requests and provide insights into the patterns of selection bias in data donation studies.
Mr Marc Asensio Manjon (University of Lausanne) - Presenting Author
Mr Oriol J. Bosch (The London School of Economics)
Dr Caroline Roberts (University of Lausanne)
Given the widespread adoption of smartphones, it is vital to understand smartphone-usage patterns, and their effect on online and offline phenomena such as mental wellbeing. Although survey self-reports are used as the main instrument to obtain smartphone usage estimates, evidence has led researchers to doubt about their quality. Hence, there is an increasing interest in directly observing individual’s behaviors using digital trace data. A relatively unexplored alternative to collect individuals’ digital traces, known as data donations, is to ask participants to share data that their devices and services already collect from them, during the course of the survey. This should stablish a transparent and self-administrable dynamic that tracking software procedures do not offer.
Data donations can take different forms, all with their own challenges, the most prominent being the expected low willingness to engage in a task that might be perceived as burdensome and sensitive. In this study, we specifically focus on the challenges and best practices when collecting already saved information about participants’ daily screentime, number of pickups and specific app usage, as reported in ‘Digital Wellbeing (Android) / Screentime (IOS)’ tools of their smartphones.
To study this, we conducted a 2x3 between-subject survey experiment in Switzerland using an online panel (N = 872). Specifically, respondents were randomly asked to share this information in three separate ways: (1) taking and uploading several screenshots of the tools; (2) taking and uploading several video recordings; and (3) manually checking and reporting the information. Participants who complied were randomly selected to receive either 10 or 20chf.
We compare the compliance and breakoff rates between experimental groups, their survey experience and engagement, and examine the reasons behind noncompliance. Additionally, we also assess the effect of the different incentive strategies. On this basis, we draw conclusions about the best approach for
Ms Henning Silber (GESIS) - Presenting Author
Dr Johannes Breuer (GESIS)
Dr Jessica Daikeler (GESIS)
Dr Barbara Felderer (GESIS)
Mr Frederic Gerdon (University of Mannheim)
Professor Florian Keusch (University of Mannheim)
Dr Bernd Weiß (GESIS)
Digital trace data are increasingly used in the social sciences. As access to these data via platform APIs can be unreliable, a method for collecting digital trace data that has gained in popularity is data donation. Behind this background, this study applied a vignette experiment to investigate the general acceptability and personal willingness to share various data types (i.e., GPS, web tracking, LinkedIn, Facebook, and TikTok) for research purposes. Besides acceptability vs. willingness and the five data types, we also experimentally varied attributes of the sharing request related to with whom the data would be shared (third-party, own project, not mentioned) and whether privacy information about the security of the data sharing process was given (provided, not mentioned), resulting in 60 hypothetical data sharing scenarios. As additional respondent-level factors potentially influencing data sharing acceptability and willingness, we asked about data usage behaviors, privacy concerns, and attitudes toward open science. The study was implemented in the probability-based German Internet Panel (GIP) in 2022, and the final sample includes about 3,800 respondents. Preliminary results show statistically significant differences between how acceptable people consider data sharing practices and how willing they are to share their own data with respect to different data types. Specifically, people show higher ratings of data sharing acceptability compared to the willingness to share their own data. Regarding the different data types, people reported that they would be more willing to share their LinkedIn and TikTok data than their GPS, web tracking, and Facebook data. A possible reason is that the latter data types are considered more private and sensitive. In contrast, with whom the data would be shared and whether information about data security was provided did not affect the responses to the respective sharing scenarios.
Dr Bella Struminskaya (Utrecht University) - Presenting Author
Dr Laura Boeschoten (Utrecht University)
Mr Joris Mulder (Centerdata)
Dr Rense Corten (Utrecht University)
Mr Stein Jongerius (Centerdata)
Dr Adriënne Mendrik (Eyra)
Digital traces that people leave on social media, by using smartphones and wearables, browsing and searching online allow us to observe individuals in-situ and offer unique opportunities for studying social reality. Some legislatures (e.g., GDPR) allow individuals to request information about themselves from the gathering organizations. Most major private data processing entities (e.g., social media platforms, search engines, energy providers, online shops) comply with this right to data access by providing Data Download Packages (DDPs) to requesting individuals. These data then can be shared with researchers. The donated data is less susceptible to social desirability and recall biases than self-report and when combined with in-the-moment questionnaires allows to study behavior and attitudes. However, data sharing might be burdensome for participants, potentially introducing selection bias. If those who donate differ from those who do not, research conclusions can be biased. We implemented a workflow allowing to collect digital traces as DDPs within two Dutch online panels. Respondents requested their DDP, storing it on their own device. DDPs were then locally processed to extract relevant variables using a specifically developed software, after which respondents viewed the extracted data and consented to sharing. We will describe the workflow of integrating data donation into online surveys and the results of a randomized experiment about the mechanisms of consent to donate Google location history and WhatsApp data. We randomly assigned respondents to the conditions: showing an example visualization similar to the one shown prior to donation to increase understanding vs. no visualization; checking the understanding of the donation request; incentive amount for donating data. We focus on willingness to donate and actual upload of the data extracted from Google location history/WhatsApp DDPs. We study selection biases by comparing the characteristics of those who donate and who do not.