Asking for Traces: A Vignette Study on Acceptability Norms and Personal Willingness to Donate Digital Trace Data

IF 3 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Social Science Computer Review Pub Date : 2024-12-09 DOI:10.1177/08944393241305776
Henning Silber, Johannes Breuer, Barbara Felderer, Frederic Gerdon, Patrick Stammann, Jessica Daikeler, Florian Keusch, Bernd Weiß
{"title":"Asking for Traces: A Vignette Study on Acceptability Norms and Personal Willingness to Donate Digital Trace Data","authors":"Henning Silber, Johannes Breuer, Barbara Felderer, Frederic Gerdon, Patrick Stammann, Jessica Daikeler, Florian Keusch, Bernd Weiß","doi":"10.1177/08944393241305776","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"83 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Social Science Computer Review","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1177/08944393241305776","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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
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。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
Exploring the Use of a Large Language Model for Inductive Content Analysis in a Discourse Network Analysis Study Response Times and Self-Reporting: Response Patterns Across Countries and World Regions Using Data From a Large Scale Computer-Based Assessment Anything but Politics: Connectedness in Networked Social Groups for Addressing Prejudice AI Chatbots in Political Campaigns: A Practical Experience in the EU’s 2024 Parliament Elections The Use of Religion Online by Indian Political Entities During the 2024 Lok Sabha Election: Religiopolitical Propaganda on Social Media?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1