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dc.contributor.advisorSamuel Madden.en_US
dc.contributor.authorTong, Cheng Hauen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-03-05T15:56:38Z
dc.date.available2014-03-05T15:56:38Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/85229
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 51-52).en_US
dc.description.abstractWith its popularity, Twitter has become an increasingly valuable source of real-time, user-generated information about interesting events in our world. This thesis presents TwitGeo, a system to explore and visualize trending topics on Twitter. It features an interactive map that summarizes trends across dierent geographical regions. Powered by a novel GPU-based datastore, this system performs ad hoc trend detection without predefined temporal or geospatial indexes, and is capable of discovering trends with arbitrary granularity in both dimensions. An evaluation of the system shows promising results for visualizing trends on Twitter in real time.en_US
dc.description.statementofresponsibilityby Cheng Hau Tong.en_US
dc.format.extent52 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleVisualizing Trends on Twitteren_US
dc.title.alternativeSystem for automated trending event coverage on Twitteren_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc871036935en_US


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