“Becoming an expert in driving for Uber”: Uber driver/bloggers’ performance of expertise and self-presentation on YouTube
Abstract
There is a new cast of self-proclaimed experts offering “how-to-succeed” resources aimed at coaching and inspiring gig workers. The emergence of such resources raises questions about the performance of expertise regarding the workings of algorithmic labor platforms. This article examines how Uber driver/bloggers—workers who are driving for Uber, while also creating Uber-related video content—perform expertise in driving for Uber on YouTube. I conducted in-depth interviews with 11 driver/bloggers and a qualitative analysis of the textual and video content published by driver/bloggers. Through the data, I show how driver/bloggers’ empowerment narratives became intertwined with their individualistic aspirations to develop dual careers as Uber drivers and YouTubers. Driver/bloggers employed three self-presentation strategies to perform expertise, including the construction of uniqueness and “know-how,” realness, and relatability with audiences. The study concludes with implications for our collective understandings of gig workers, expertise, and online curation across a wider platform ecology.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The rating system serves as an engine of anxiety that shapes workers' ongoing work practices.
“As such, the rating system serves as an engine of anxiety that shapes workers' ongoing work practices (Chan, 2019;Espeland et al., 2016).”
From Gig work and gig workers: An integrative review and agenda for future research (2024) · cites this paper as Chan, 2019
Cites
(Not) Getting Paid to Do What You Love: Gender, Social Media, and Aspirational Work
Duffy, B. E. (2017) · Yale University Press · doi:10.12987/yale/9780300218176.001.0001 · Cited by 931 · see citing works
<i>Knock, Knock</i> . Who's There? The Imagined Audience
Litt, E. (2012) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2012.705195 · Cited by 629 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Gig work and gig workers: An integrative review and agenda for future research
Wu, D., & Huang, J. L. (2024) · Journal of Organizational Behavior · doi:10.1002/job.2775 · Cited by 168 · see citing works
Chan, N. K. (2022) · Big Data & Society · doi:10.1177/20539517221133779 · Cited by 48 · see citing works
Self-citation
- Authors
- Chan, N. K.
- Affiliations
- Cornell University (US)
- Corresponding author
- Chan, Ngai Keung
- ORCID
- Chan, Ngai Keung
- Venue
- New Media & Society
- Publisher
- SAGE Publications
- Published
- 2019-04
- Volume
- 21
- Issue
- 9
- Pages
- 2048-2067
- ISSN
- 1461-4448 (ISSN-L)
- 1461-7315
- OpenAlex
- W2926114267
- Cited by
- 64
- References
- 2
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 11 · 2022: 17 · 2021: 12 · 2020: 5 · 2019: 2
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- New Media & Society (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Gender Studies › Gender, Feminism, and Media
- Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
- Keywords
- Uber drivers, YouTube, self-presentation, gig work, empowerment narratives, dual career, know-how, relatability, platform ecology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review