Online social network sensors for influenza outbreaks

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Everett, Katie Elizabeth

Advisor dc:contributor.advisor

• Munther Dahleh.

• Electrical Engineering and Computer Science.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/85416

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/85416