Algorithms for Modeling Mass Movements and their Adoption in Social Networks
General Material Designation
[Thesis]
First Statement of Responsibility
Fang Jin
Subsequent Statement of Responsibility
Ramakrishnan, Narendran
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
Virginia Polytechnic Institute and State University
Date of Publication, Distribution, etc.
2016
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
125
GENERAL NOTES
Text of Note
Committee members: Cao, Yang; Chen, Feng; Lu, Chang-Tien; North, Chris
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Place of publication: United States, Ann Arbor; ISBN=978-0-355-34076-1
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
Ph.D.
Discipline of degree
Computer Science
Body granting the degree
Virginia Polytechnic Institute and State University
Text preceding or following the note
2016
SUMMARY OR ABSTRACT
Text of Note
Online social networks have become a staging ground for many modern movements, with the Arab Spring being the most prominent example. In an effort to understand and predict those movements, social media can be regarded as a valuable social sensor for disclosing underlying behaviors and patterns. To fully understand mass movement information propagation patterns in social networks, several problems need to be considered and addressed. Specifically, modeling mass movements that incorporate multiple spaces, a dynamic network structure, and misinformation propagation, can be exceptionally useful in understanding information propagation in social media.