Detecting Events From Twitter In Real-Time

Simple item page
dc.contributor.advisorZhong, Lin
dc.contributor.committeeMemberSabharwal, Ashutosh
dc.contributor.committeeMemberSubramanian, Devika
dc.contributor.committeeMemberVasuderan, Venu
dc.creatorZhao, Siqi
dc.date.accessioned2013-09-16T19:15:19Z
dc.date.accessioned2013-09-16T19:15:21Z
dc.date.available2013-09-16T19:15:19Z
dc.date.available2013-09-16T19:15:21Z
dc.date.created2013-05
dc.date.issued2013-09-16
dc.date.submittedMay 2013
dc.date.updated2013-09-16T19:15:21Z
dc.description.abstractTwitter is one of the most popular online social networking sites. It provides a unique and novel venue of publishing: it has over 500 million active users around the globe; tweets are brief, limited to 140 characters, an ideal way for people to publish spontaneously. As a result, Twitter has the short delays in reflecting what its users perceive, compared to other venues such as blogs and product reviews. We design and implement SportSense, which exploits Twitter users as human sensors of the physical world to detect major events in real-time. Using the National Football League (NFL) games as a targeted domain, we report in-depth studies of the delay and trend of tweets, and their dependence on other properties. We present event detection method based on these findings, and demonstrate that it can effectively and accurately extract major game events using open access Twitter data. SportSense has been evolving during the 2010-11 and 2011-12 NFL seasons and it has been collecting hundreds of millions tweets. We provide SportSense API for developers to use our system to create Twitter-enabled applications.
dc.format.mimetypeapplication/pdf
dc.identifier.citationZhao, Siqi. "Detecting Events From Twitter In Real-Time." (2013) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/72070">https://hdl.handle.net/1911/72070</a>.
dc.identifier.slug123456789/ETD-2013-05-358
dc.identifier.urihttps://hdl.handle.net/1911/72070
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectSocial networks
dc.subjectTwitter
dc.subjectComputer engineering
dc.titleDetecting Events From Twitter In Real-Time
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ZHAO-THESIS.pdf
Size:
1.39 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.61 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Rice University Electronic Theses and Dissertations
ECE Theses and Dissertations