Online social network sensors for influenza outbreaks
Name
870527362-MIT.pdf
Description
Full printable version
Size
668.11 KB
Format
Adobe PDF
Checksum (MD5)
83ec0bcaa0ee8c6cff49ae7a9fd5df2d
Author(s)
Everett, Katie Elizabeth
Advisor(s)
Munther Dahleh.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
Previous research has shown strong correlations between postings on the online social network Twitter where users complain of influenza-like symptoms, and clinical data on actual influenza rates. In addition, previous research has shown that more popular individuals in a real-life social network are infected with influenza earlier than average individuals. We collect all flu-related tweets during the 2012-2013 influenza season in order to compare the timing of flu-related tweets from more popular users compared to less popular users. No difference is seen in flu tweet timing between Twitter users with a high number of followers compared to users with a low number of followers. Restricting the Twitter network to bidirectional edges (mutual followings) performs slightly better, but is still not significant. Future work should focus on identifying edges in online social networks that indicate that two users regularly come into close physical proximity.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 27-28).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
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