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The evolution of the gig worker career through keyword extraction from text mining

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  • Lang, Jiao-jiao
  • Sun, Xiu-li
  • Yang, Li-feng
  • Cheng, Chen
  • Cheng, Xiang-yang

Abstract

The gig economy, enabled by digital platforms and algorithmic management, is reshaping how careers unfold. This study applies Super's career development theory to analyze over 380,000 rider comments, using LDA topic modeling, part-of-speech tagging, and feature matching. The research identifies four distinct career stages: exploration (18.5%), establishment (59.5%), maintenance (13.5%), and decline (8.5%). Key findings reveal that: (1) riders' discourse centers on platform mechanisms and self-management; (2) exploratory, rule-learning talk persists well into the establishment stage, consistent with limited structured career guidance in platform work; and (3) the decline stage exhibits complex affective patterns, combining high negativity (45.6%) with continued mentoring and peer support. These findings illuminate stage-specific tensions in gig workers' career development under algorithmic governance and offer implications for platform design and worker-support interventions.

Suggested Citation

  • Lang, Jiao-jiao & Sun, Xiu-li & Yang, Li-feng & Cheng, Chen & Cheng, Xiang-yang, 2026. "The evolution of the gig worker career through keyword extraction from text mining," Technology in Society, Elsevier, vol. 87(C).
    • Handle: RePEc:eee:teinso:v:87:y:2026:i:c:s0160791x26001260
    DOI: 10.1016/j.techsoc.2026.103337
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