The view from somewhere

Critically reflexive recruitment heuristics for big data

Authors

  • Corinne Jones University of Central Florida

DOI:

https://doi.org/10.5210/fm.v27i2.12276

Keywords:

research methods, research ethics, big data, digital humanities, feminist

Abstract

Recent scholarship has raised important critical questions about the ethical uses of big data scraped from social media platforms. Critical and feminist researchers have argued that big data can naturalize a “view from nowhere,” which ultimately reinscribes the status quo. In response, researchers have sought to incorporate critical reflexivity into their big data research and to include participants in their research through surveys and interviews. With surveys and interviews, researchers must engage in critically reflexive recruitment practices though, as reaching out to participants always reveals a “view from somewhere” that can (but does not always) function in the milieu of the dominant gaze. This article builds on scholarship that calls for more ethical data practices by offering three heuristics for critical reflexive recruitment practices. First, researchers should consider whether to recruit participants based on shifting boundaries of publicity and privacy. They should evaluate if posts were meant for their particular “view from somewhere.” Second, researchers should assess the names of research accounts, and how the names might be experienced as extractive. Finally, researchers should critically reflect on channels of recruitment and how those channels function in public/private binaries.

Author Biography

Corinne Jones, University of Central Florida

Corinne Jones (Ph.D.) is a visiting lecturer at the University of Central Florida whose research interests include digital rhetoric and circulation, social media, critical race and gender studies, digital humanities, and digital research.

Downloads

Published

2022-02-14

How to Cite

Jones, C. (2022). The view from somewhere: Critically reflexive recruitment heuristics for big data. First Monday, 27(2). https://doi.org/10.5210/fm.v27i2.12276