A community rather than a union: Understanding self-organization phenomenon on MTurk and how it impacts turkers and requesters

Xinyi Wang, Haiyi Zhu, Yangyun Li, Yu Cui, Joseph Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper aims to understand the self-organization phenomenon among the workers of Amazon Mechanical Turk (MTurk), a well-known crowdsourcing platform. Specifically, we explored 1) why MTurk workers self-organize into online communities (Turker communities), and 2) how the workers' self-organization impacts the requesters and 3) the workers. In the first study, we conducted a field experiment by advertising the same survey tasks on both MTurk and on Turker communities. In the second study, we interviewed two founders of the Turker communities. The results suggest that 1) workers' main motivation to participate in communities is to "find good HITs". 2) For requesters, there is no indication of differences in work quality between the tasks posted on MTurk and the ones advertised on Turker communities. 3) For workers, participation in Turker communities is associated with higher income, controlled for working hours. We also learned from the interviews about why founders built the communities and their future plans.

Original languageEnglish (US)
Title of host publicationCHI 2017 Extended Abstracts - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery
Pages2210-2216
Number of pages7
ISBN (Electronic)9781450346566
DOIs
StatePublished - May 6 2017
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017 - Denver, United States
Duration: May 6 2017May 11 2017

Publication series

NameConference on Human Factors in Computing Systems - Proceedings
VolumePart F127655

Other

Other2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI EA 2017
Country/TerritoryUnited States
CityDenver
Period5/6/175/11/17

Keywords

  • Amazon Mechanical Turk
  • Crowdsourcing
  • Online community
  • Self-organization
  • Work quality
  • Working condition

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