Online social network sensors for influenza outbreaks
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2014
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| Online Access: | http://hdl.handle.net/1721.1/85416 |
| _version_ | 1826206740918566912 |
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| author | Everett, Katie Elizabeth |
| author2 | Munther Dahleh. |
| author_facet | Munther Dahleh. Everett, Katie Elizabeth |
| author_sort | Everett, Katie Elizabeth |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. |
| first_indexed | 2024-09-23T13:37:31Z |
| format | Thesis |
| id | mit-1721.1/85416 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:37:31Z |
| publishDate | 2014 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/854162019-04-11T03:16:22Z Online social network sensors for influenza outbreaks Everett, Katie Elizabeth Munther Dahleh. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Katie Elizabeth Everett. M. Eng. 2014-03-06T15:40:29Z 2014-03-06T15:40:29Z 2013 2013 Thesis http://hdl.handle.net/1721.1/85416 870527362 eng 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 28 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Everett, Katie Elizabeth Online social network sensors for influenza outbreaks |
| title | Online social network sensors for influenza outbreaks |
| title_full | Online social network sensors for influenza outbreaks |
| title_fullStr | Online social network sensors for influenza outbreaks |
| title_full_unstemmed | Online social network sensors for influenza outbreaks |
| title_short | Online social network sensors for influenza outbreaks |
| title_sort | online social network sensors for influenza outbreaks |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/85416 |
| work_keys_str_mv | AT everettkatieelizabeth onlinesocialnetworksensorsforinfluenzaoutbreaks |