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- W1534872013 abstract "Previous research has shown strong correlationsbetween postings on the online social network Twitter where userscomplain of influenza-like symptoms, and clinical data on actualinfluenza rates. In addition, previous research has shown that morepopular individuals in a real-life social network are infected withinfluenza earlier than average individuals. We collect allflu-related tweets during the 2012-2013 influenza season in orderto compare the timing of flu-related tweets from more popular userscompared to less popular users. No difference is seen in flu tweettiming between Twitter users with a high number of followerscompared to users with a low number of followers. Restricting theTwitter network to bidirectional edges (mutual followings) performsslightly better, but is still not significant. Future work shouldfocus on identifying edges in online social networks that indicatethat two users regularly come into close physicalproximity." @default.
- W1534872013 created "2016-06-24" @default.
- W1534872013 creator A5085045392 @default.
- W1534872013 date "2013-01-01" @default.
- W1534872013 modified "2023-09-27" @default.
- W1534872013 title "Online social network sensors for influenza outbreaks" @default.
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