Abstract
Objective: Big social data (such as social media and blogs) and archived qualitative data (such as interview transcripts, field notebooks, and diaries) are similar, but their respective communities of practice are under-connected. This paper explores shared challenges in qualitative data reuse and big social research and identifies implications for data curation. Methods: This paper uses a broad literature search and inductive coding of 300 articles relating to qualitative data reuse and big social research. The literature review produces six key challenges relating to data use and reuse that are present in both qualitative data reuse and big social research—context, data quality, data comparability, informed consent, privacy & confidentiality, and intellectual property & data ownership. Results: This paper explores six key challenges related to data use and reuse for qualitative data and big social research and discusses their implications for data curation practices. Conclusions: Data curators can benefit from understanding these six key challenges and examining data curation implications. Data curation implications from these challenges include strategies for: providing clear documentation; linking and combining datasets; supporting trustworthy repositories; using and advocating for metadata standards; discussing alternative consent strategies with researchers and IRBs; understanding and supporting deidentification challenges; supporting restricted access for data; creating data use agreements; supporting rights management and data licensing; developing and supporting alternative archiving strategies. Considering these data curation implications will help data curators support sounder practices for both qualitative data reuse and big social research.
Highlights
Big social data and archived qualitative data are similar, but their respective communities of practice are under-connected. Research with both types of data repurpose existing data to advance discoveries in social science. Despite these similarities, big social research has not yet been widely framed as a form of qualitative data reuse, and qualitative data reuse has only begun to be discussed through a big social data lens
This paper explores six key issues that are present in both big social research and qualitative data reuse, and outlines implications for data curation practices related to each issue
This paper draws upon the above definitions of qualitative data reuse and big social research: Big social research is when researchers use existing data from social media or other online social environments to gain insights and produce scholarship
Summary
Using the methods outlined by Creswell (2009) and in more detail in the Handbook of Research Synthesis and Meta-analysis For both qualitative data reuse and big social research, data curators can support comparability by encouraging researchers who publish data to include clear documentation to address missing data, research questions, and methods. The literature suggests that if data curators can reach investigators early in the research process, they can help provide guidance for alternative consent strategies for qualitative data reuse and big social research. Comparing the identifiability of traditional qualitative research with that of big social research, Chu et al point out that while it is common in qualitative studies to directly quote respondents in order to support key findings and highlight ideas of interest, the full-text indexing of social media platforms may cause any direct quotes to be identifiable (Chu et al 2019) For both qualitative data reuse and big social research, privacy should be more carefully considered when the research involves vulnerable populations (Clark et al 2018), for whom reidentification could be especially damaging. Data curators can encourage inclusion of tools such as the Twitter Hydrator as part of the data deposit, to support usability for the archived data (Kinder-Kurlanda et al 2017)
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