Data-driven Crowdsourcing via Online Social Users

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Data-driven Crowdsourcing via Online Social Users"

By

Mr. Chen CAO


Abstract

Human computation is a long-existing concept and has been practiced for 
centuries. Specifically, whenever a human serves to compute, human 
computation is observed. This leads to a history of Human Computation even 
longer than that of electronic computer. Now with the development of 
Internet web service, the workforce of human computation is broadened to a 
vast pool of crowds, e.g. Amazon Mechanic Turk, instead of designated 
exerts or employees. This type of outsourcing to crowds, a.k.a. 
crowdsourcing, ushers in the new computation paradigm of Crowdsourced 
Human Computation. Data-driven applications also benefit from the 
crowdsourcing power, where the crowds are utilized as a data processing 
module.

However, traditional crowd-powered task processing relies on centralized 
platforms. These markets are specially designed based on a labor market 
structure, which facilitates the task display and post-task payoff. But 
such mechanism also constrains the source of crowd workforce, which leads 
to difficulties in terms of quality control, cost management, as well as 
bias of worker demographics.

In this thesis, we elaborate the effort of employing online social users 
as another source of crowdsourcing workforce. We show that a most of 
data-driven applications can be decomposed into binary decision making or 
information elicitation tasks for human workforce. Then we illustrate the 
majority voting over the decision making as crowdsourced answer aggregator 
and discuss its properties. Moreover, there are three major challenges to 
establish high-performance crowdsourcing applications onto online social 
users as crowdsourcing workforce; therefore we present corresponding 
techniques as follows:
Quality: Jury-selection algorithms to solve "Whom to Ask" challenge to 
improve answer quality under majority voting;
Cost: WiseMarket as a new crowdsourcing paradigm to conduct payment with 
less cost and higher quality;
Authenticity: COPE as an approach to elicit opinion from online crowds 
with authenticity guarantee and cost control.

In the end, we show directions of future work in applying the data-driven 
crowdsourcing via online social users.


Date:			Thursday, 17 July 2014

Time:			2:00pm - 4:00pm

Venue:			Room 3501
 			Lifts 25/26

Chairman:		Prof. Jianan Qu (ECE)

Committee Members:	Prof. Lei Chen (Supervisor)
 			Prof. Huamin Qu
 			Prof. Raymond Wong
 			Prof. Xuhu Wan (ISOM)
                        Prof. Qing Li (Comp. Sci., CityU)


**** ALL are Welcome ****