{"title":"When research is the context: Cross-platform user expectations for social media data reuse","authors":"Sarah A. Gilbert, Katie Shilton, Jessica Vitak","doi":"10.1177/20539517231164108","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":" ","pages":""},"PeriodicalIF":6.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data & Society","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1177/20539517231164108","RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
Abstract
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.
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.