| dc.contributor.advisor | Samuel Madden. | en_US |
| dc.contributor.author | Tong, Cheng Hau | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2014-03-05T15:56:38Z | |
| dc.date.available | 2014-03-05T15:56:38Z | |
| dc.date.copyright | 2013 | en_US |
| dc.date.issued | 2013 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/85229 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. | en_US |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 51-52). | en_US |
| dc.description.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. | en_US |
| dc.description.statementofresponsibility | by Cheng Hau Tong. | en_US |
| dc.format.extent | 52 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | 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. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Visualizing Trends on Twitter | en_US |
| dc.title.alternative | System for automated trending event coverage on Twitter | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 871036935 | en_US |
