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CrowdMAQA

Motivation and Automatic Quality Assessment in Paid Crowdsourcing Online Labor Markets

 

Description:

 

Building on the growth in availability and popularity of crowd-sourced data, this project will answer essential questions about data quality and characteristics of crowd-sourced data.  It will focus on how to assess, monitor and assure the data quality in the context of crowdsourcing and examine factors influencing the motivation of workers.

Regardless of the growing popularity of crowd sourcing, one essential problem remains untackled.  Anonymously paid micro tasks are frequently corrupted by certain proportion of subjects who do not focus enough on the job or who do not work as instructed but give random data. This in turn leads to noisy responses or inaccurate results and thus to a considerable deterioration of the data quality.

In this project, we are intended to answer the following research questions:

  • How can we measure and predict the quality of responses to predefined online jobs such as survey, multimedia recording or free-text responses?
  • What is the relationship between the motivation of an online worker and performance, and how can a worker’s motivation be influenced?

 

 

Time Frame:
4/2012 - 3/2015
Team Members:
Babak Naderi
Software Campus Partners:
TU-Berlin, Deutsche Telekom AG
Funding by:
Bundesministerium für Bildung und Forschung - BMBF

Pubications

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