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Algorithms for Modeling Mass Movements and their Adoption in Social Networks

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dc.contributor.authorJin, Fangen
dc.contributor.committeechairRamakrishnan, Narenen
dc.contributor.committeememberCao, Yangen
dc.contributor.committeememberNorth, Christopher L.en
dc.contributor.committeememberLu, Chang-Tienen
dc.contributor.committeememberChen, Fengen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2016-08-24T08:00:51Zen
dc.date.available2016-08-24T08:00:51Zen
dc.date.issued2016-08-23en
dc.description.abstractOnline 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. This dissertation explores four research problems underlying efforts to identify and track the adoption of mass movements in social media. First, how do mass movements become mobilized on Twitter, especially in a specific geographic area? Second, can we detect protest activity in social networks by observing group anomalies in graph? Third, how can we distinguish real movements from rumors or misinformation campaigns? and fourth, how can we infer the indicators of a specific type of protest, say climate related protest? A fundamental objective of this research has been to conduct a comprehensive study of how mass movement adoption functions in social networks. For example, it may cross multiple spaces, evolve with dynamic network structures, or consist of swift outbreaks or long term slowly evolving transmissions. In many cases, it may also be mixed with misinformation campaigns, either deliberate or in the form of rumors. Each of those issues requires the development of new mathematical models and algorithmic approaches such as those explored here. This work aims to facilitate advances in information propagation, group anomaly detection and misinformation distinction and, ultimately, help improve our understanding of mass movements and their adoption in social networks.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:8614en
dc.identifier.urihttp://hdl.handle.net/10919/72292en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectMass Movementsen
dc.subjectGroup Anomaliesen
dc.subjectEvent Detectionen
dc.subjectInformation Propagationen
dc.subjectSocial Networksen
dc.titleAlgorithms for Modeling Mass Movements and their Adoption in Social Networksen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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