4 research outputs found
Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions
Crowdsourcing enables one to leverage on the intelligence and wisdom of
potentially large groups of individuals toward solving problems. Common
problems approached with crowdsourcing are labeling images, translating or
transcribing text, providing opinions or ideas, and similar - all tasks that
computers are not good at or where they may even fail altogether. The
introduction of humans into computations and/or everyday work, however, also
poses critical, novel challenges in terms of quality control, as the crowd is
typically composed of people with unknown and very diverse abilities, skills,
interests, personal objectives and technological resources. This survey studies
quality in the context of crowdsourcing along several dimensions, so as to
define and characterize it and to understand the current state of the art.
Specifically, this survey derives a quality model for crowdsourcing tasks,
identifies the methods and techniques that can be used to assess the attributes
of the model, and the actions and strategies that help prevent and mitigate
quality problems. An analysis of how these features are supported by the state
of the art further identifies open issues and informs an outlook on hot future
research directions.Comment: 40 pages main paper, 5 pages appendi
A Transfer-Learning based Framework of Crowd-Selection on Twitter
Crowd selection is essential to crowd sourcing applications, since choosing the right workers with particular expertise to carry out crowdsourced tasks is extremely important. The central problem is simple but tricky: given a crowdsourced task, who are the most knowledgable users to ask? In this demo, we show our framework that tackles the problem of crowdsourced task assignment on Twitter according to the social activities of its users. Since user profiles on Twitter do not reveal user interests and skills, we transfer the knowledge from categorized Yahoo ! Answers datasets for learning user expertise. Then, we select the right crowd for certain tasks based on user expertise. We study the effectiveness of our system using extensive user evaluation. We further engage the attendees to participate a game called“Whom to Ask on Twitter”. This helps understand our ideas in an interactive manner. Our crowd selection can be accessed by the following url http://webproject2.cse.ust.hk:8034/tcrowd/