当研究是背景时:跨平台用户对社交媒体数据重用的期望

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Big Data & Society Pub Date : 2023-01-01 DOI:10.1177/20539517231164108
Sarah A. Gilbert, Katie Shilton, Jessica Vitak
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引用次数: 1

摘要

社交媒体为研究人员了解各种现象提供了独特的机会——它通常是公开可用的,易于获取,并且提供了更自然的观察。然而,随着使用社交媒体数据的研究越来越多,公众监督也越来越多,这凸显了开发道德方法来使用社交媒体数据的必要性。在这一领域之前的工作已经探索了用户对研究人员在单一平台背景下使用社交媒体数据的看法。在本文中,我们扩展了这项工作,探索平台及其功能如何影响用户对社交媒体数据重用的感受。我们展示了三个因子小插曲调查的结果,每个调查都集中在一个不同的平台上——约会应用程序、Instagram和reddit——以评估用户在各种环境下对研究数据使用场景的舒适度。尽管我们的研究结果强调了不同平台之间的不同期望,这取决于研究领域、研究目的和收集的内容,但我们发现,在所有平台上影响最大的因素是同意——这一发现给大数据研究人员带来了挑战。最后,我们提供了一种社会技术方法来进行道德决策。这种方法为研究人员如何解释和响应平台规范和能力提供了建议,以预测潜在的数据使用敏感性。该方法还建议研究人员通过加强对数字平台上数据收集的认识来回应对参与研究的通知和同意的主要期望。
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When research is the context: Cross-platform user expectations for social media data reuse
Social media provides unique opportunities for researchers to learn about a variety of phenomena—it is often publicly available, highly accessible, and affords more naturalistic observation. However, as research using social media data has increased, so too has public scrutiny, highlighting the need to develop ethical approaches to social media data use. Prior work in this area has explored users’ perceptions of researchers’ use of social media data in the context of a single platform. In this paper, we expand on that work, exploring how platforms and their affordances impact how users feel about social media data reuse. We present results from three factorial vignette surveys, each focusing on a different platform—dating apps, Instagram, and Reddit—to assess users’ comfort with research data use scenarios across a variety of contexts. Although our results highlight different expectations between platforms depending on the research domain, purpose of research, and content collected, we find that the factor with the greatest impact across all platforms is consent—a finding which presents challenges for big data researchers. We conclude by offering a sociotechnical approach to ethical decision-making. This approach provides recommendations on how researchers can interpret and respond to platform norms and affordances to predict potential data use sensitivities. The approach also recommends that researchers respond to the predominant expectation of notification and consent for research participation by bolstering awareness of data collection on digital platforms.
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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