Analyzing Cyber-Enabled Social Movement Organizations: A Case Study with Crowd-Powered Search

Persistent Link:
http://hdl.handle.net/10150/265358
Title:
Analyzing Cyber-Enabled Social Movement Organizations: A Case Study with Crowd-Powered Search
Author:
Zhang, Qingpeng
Issue Date:
2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The advances in social media and social computing technologies have dramatically changed the way through which people interact, organize, and collaborate. The use of social media also makes the large-scale data revealing human behavior accessible to researchers and practitioners. The analysis and modeling of social networks formed from relatively stable online communities have been extensively studied. The research on the structural and dynamical patterns of large-scale crowds motivated by accomplishing common goals, named the cyber movement organizations (CMO) or cyber-enabled social movement organizations (CeSMO), however, is still limited to anecdotal case studies. This research is one of the first steps towards the understanding of the CMO/CeSMO based on real data collected from online social media.The focus of my research is on the study of an important type of CMO/CeSMO, the crowd-powered search behavior (also known as human flesh search, HFS), in which a large number of Web users voluntarily gathered together to find out the truth of an event or the information of a person that could not be identified by one single person or simple online searches. In this research, I have collected a comprehensive data-set of HFS. I first introduce the phenomenon of HFS and reviewed the study of online social groups/communities. Then, I present the empirical studies of both individual HFS episodes and aggregated HFS communities, and unveiled their unique topological properties. Based on the empirical findings, I propose two models to simulate evolution and topology of individual HFS networks. I conclude the dissertation with discussions of future research of CMO/CeSMO.
Type:
text; Electronic Dissertation
Keywords:
Science of Team Science; Social Computing; Social Movement Organizations; Social Network Analysis; Systems & Industrial Engineering; Collective Intelligence; Network Science
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Systems & Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Zeng, Daniel Dajun; Lin, Wei Hua

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleAnalyzing Cyber-Enabled Social Movement Organizations: A Case Study with Crowd-Powered Searchen_US
dc.creatorZhang, Qingpengen_US
dc.contributor.authorZhang, Qingpengen_US
dc.date.issued2012-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe advances in social media and social computing technologies have dramatically changed the way through which people interact, organize, and collaborate. The use of social media also makes the large-scale data revealing human behavior accessible to researchers and practitioners. The analysis and modeling of social networks formed from relatively stable online communities have been extensively studied. The research on the structural and dynamical patterns of large-scale crowds motivated by accomplishing common goals, named the cyber movement organizations (CMO) or cyber-enabled social movement organizations (CeSMO), however, is still limited to anecdotal case studies. This research is one of the first steps towards the understanding of the CMO/CeSMO based on real data collected from online social media.The focus of my research is on the study of an important type of CMO/CeSMO, the crowd-powered search behavior (also known as human flesh search, HFS), in which a large number of Web users voluntarily gathered together to find out the truth of an event or the information of a person that could not be identified by one single person or simple online searches. In this research, I have collected a comprehensive data-set of HFS. I first introduce the phenomenon of HFS and reviewed the study of online social groups/communities. Then, I present the empirical studies of both individual HFS episodes and aggregated HFS communities, and unveiled their unique topological properties. Based on the empirical findings, I propose two models to simulate evolution and topology of individual HFS networks. I conclude the dissertation with discussions of future research of CMO/CeSMO.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectScience of Team Scienceen_US
dc.subjectSocial Computingen_US
dc.subjectSocial Movement Organizationsen_US
dc.subjectSocial Network Analysisen_US
dc.subjectSystems & Industrial Engineeringen_US
dc.subjectCollective Intelligenceen_US
dc.subjectNetwork Scienceen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems & Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorZeng, Daniel Dajunen_US
dc.contributor.advisorLin, Wei Huaen_US
dc.contributor.committeememberWang, Fei-Yueen_US
dc.contributor.committeememberLin, Wei Huaen_US
dc.contributor.committeememberBreiger, Ronalden_US
dc.contributor.committeememberLiu, Jianen_US
dc.contributor.committeememberZeng, Daniel Dajunen_US
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