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dc.contributor.authorHua, Tingen
dc.date.accessioned2018-02-06T09:00:50Zen
dc.date.available2018-02-06T09:00:50Zen
dc.date.issued2018-02-05en
dc.identifier.othervt_gsexam:14018en
dc.identifier.urihttp://hdl.handle.net/10919/82029en
dc.description.abstractThe rise of big data, especially social media data (e.g., Twitter, Facebook, Youtube), gives new opportunities to the understanding of human behavior. Consequently, novel computing methods for mining patterns in social media data are therefore desired. Through applying these approaches, it has become possible to aggregate public available data to capture triggers underlying events, detect on-going trends, and forecast future happenings. This thesis focuses on developing methods for social media analysis. Specifically, five directions are proposed here: 1) semi-supervised detection for targeted-domain events, 2) topical interaction study among multiple datasets, 3) discriminative learning about the identifications for common and distinctive topics, 4) epidemics modeling for flu forecasting with simulation via signals from social media data, 5) storyline generation for massive unorganized documents.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSocial mediaen
dc.subjectTopic modelingen
dc.subjectEvent Detectionen
dc.titleTopics, Events, Stories in Social Mediaen
dc.typeDissertationen
dc.contributor.departmentComputer Scienceen
dc.description.degreePh. D.en
thesis.degree.namePh. D.en
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineComputer Science and Applicationsen
dc.contributor.committeechairLu, Chang-Tienen
dc.contributor.committeememberReddy, Chandan K.en
dc.contributor.committeememberLi, Zhenhuien
dc.contributor.committeememberChen, Ing Rayen
dc.contributor.committeememberRamakrishnan, Narenen


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