询问痕迹:关于捐赠数字痕迹数据的可接受性规范和个人意愿的小研究

IF 3 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Henning Silber, Johannes Breuer, Barbara Felderer, Frederic Gerdon, Patrick Stammann, Jessica Daikeler, Florian Keusch, Bernd Weiß
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引用次数: 0

摘要

数字轨迹数据在社会科学领域的应用越来越广泛。考虑到通过应用程序编程接口(api)访问数据的风险,以及围绕使用此类数据的伦理讨论,数据捐赠已被提议作为一种方法上可靠且伦理上合理的收集数字痕迹数据的方式。虽然数据捐赠有很多好处,但研究参与者可能不愿意分享他们的数据,例如,出于隐私方面的考虑。为了评估数据捐赠请求中的哪些因素与参与者的接受和决定相关,我们进行了一个小插图实验,调查了捐赠各种数据类型(即来自GPS,网页浏览,LinkedIn/Xing, Facebook和TikTok的数据)的一般可接受性和个人意愿,用于研究目的。预注册研究在基于概率的德国互联网小组(GIP)中实施,并收集了n = 3821名参与者的反馈。结果表明,人们对数据捐赠请求的普遍可接受性的评价高于自己捐赠数据的意愿。对于不同的数据类型,受访者表示,与网页浏览和Facebook数据相比,他们更愿意捐赠他们的LinkedIn/Xing、TikTok和GPS数据。相比之下,关于捐赠数据是否会与其他研究人员共享和数据安全的信息并不影响对各自捐赠场景的反应。基于这些结果,我们讨论了采用数据捐赠研究的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Asking for Traces: A Vignette Study on Acceptability Norms and Personal Willingness to Donate Digital Trace Data
Digital trace data are increasingly used in the social sciences. Given the risks associated with data access via application programming interfaces (APIs) as well as ethical discussions around the use of such data, data donations have been proposed as a methodologically reliable and ethically sound way of collecting digital trace data. While data donations have many advantages, study participants may be reluctant to share their data, for example, due to privacy concerns. To assess which factors in a data donation request are relevant for participants’ acceptance and decisions, we conducted a vignette experiment investigating the general acceptability and personal willingness to donate various data types (i.e., data from GPS, web browsing, LinkedIn/Xing, Facebook, and TikTok) for research purposes. The preregistered study was implemented in the probability-based German Internet Panel (GIP) and gathered responses from n = 3821 participants. Results show that people rate the general acceptability of data donation requests higher than their own willingness to donate data. Regarding the different data types, respondents indicated that they would be more willing to donate their LinkedIn/Xing, TikTok, and GPS data compared to web browsing and Facebook data. In contrast, information about whether the donated data would be shared with other researchers and data security did not affect the responses to the respective donation scenarios. Based on these results, we discuss implications for studies employing data donations.
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来源期刊
Social Science Computer Review
Social Science Computer Review 社会科学-计算机:跨学科应用
CiteScore
9.00
自引率
4.90%
发文量
95
审稿时长
>12 weeks
期刊介绍: Unique Scope Social Science Computer Review is an interdisciplinary journal covering social science instructional and research applications of computing, as well as societal impacts of informational technology. Topics included: artificial intelligence, business, computational social science theory, computer-assisted survey research, computer-based qualitative analysis, computer simulation, economic modeling, electronic modeling, electronic publishing, geographic information systems, instrumentation and research tools, public administration, social impacts of computing and telecommunications, software evaluation, world-wide web resources for social scientists. Interdisciplinary Nature Because the Uses and impacts of computing are interdisciplinary, so is Social Science Computer Review. The journal is of direct relevance to scholars and scientists in a wide variety of disciplines. In its pages you''ll find work in the following areas: sociology, anthropology, political science, economics, psychology, computer literacy, computer applications, and methodology.
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