An investigation of unpaid crowdsourcing

Ria Mae Borromeo, Motomichi Toyama

Research output: Contribution to journalReview article

10 Citations (Scopus)

Abstract

The continual advancement of internet technologies has led to the evolution of how individuals and organizations operate. For example, through the internet, we can now tap a remote workforce to help us accomplish certain tasks, a phenomenon called crowdsourcing. Crowdsourcing is an approach that relies on people to perform activities that are costly or time-consuming using traditional methods. Depending on the incentive given to the crowd workers, crowdsourcing can be classified as paid or unpaid. In paid crowdsourcing, the workers are incentivized financially, enabling the formation of a robust workforce, which allows fast completion of tasks. Consequently, in unpaid crowdsourcing, the lack of financial incentive potentially leads to an unpredictable workforce and indeterminable task completion time. However, since payment to workers is not necessary, it can be an economical alternative for individuals and organizations who are more concerned about the budget than the task turnaround time. In this study, we explore unpaid crowdsourcing by reviewing crowdsourcing applications where the crowd comes from a pool of volunteers. We also evaluate its performance in sentiment analysis and data extraction projects. Our findings suggest that for such tasks, unpaid crowdsourcing completes slower but yields results of similar or higher quality compared to its paid counterpart.

Original languageEnglish
Article number11
JournalHuman-centric Computing and Information Sciences
Volume6
Issue number1
DOIs
Publication statusPublished - 2016 Dec 1

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Keywords

  • Crowdsourcing
  • Data extraction
  • Platforms
  • Sentiment analysis

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

An investigation of unpaid crowdsourcing. / Borromeo, Ria Mae; Toyama, Motomichi.

In: Human-centric Computing and Information Sciences, Vol. 6, No. 1, 11, 01.12.2016.

Research output: Contribution to journalReview article

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