2026-09-18T19:33:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/852292026-06-06T00:49:18Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Samuel Madden.
Tong, Cheng Hau
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-05T15:56:38Z
2014-03-05T15:56:38Z
2013
2013
http://hdl.handle.net/1721.1/85229
871036935
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 51-52).
With 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.
by Cheng Hau Tong.
M.Eng.
52 pages
eng
Massachusetts Institute of Technology
M.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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Visualizing Trends on Twitter
System for automated trending event coverage on Twitter
Thesis
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<Abstract>With 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.</Abstract>
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