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The reliability of tweets as a supplementary method of seasonal influenza surveillance
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نویسنده
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aslam a.a. ,tsou m.-h. ,spitzberg b.h. ,an l. ,gawron j.m. ,gupta d.k. ,peddecord k.m. ,nagel a.c. ,allen c. ,yang j.-a. ,lindsay s.
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منبع
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journal of medical internet research - 2014 - دوره : 16 - شماره : 11
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چکیده
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Background: existing influenza surveillance in the united states is focused on the collection of data from sentinel physicians and hospitals; however,the compilation and distribution of reports are usually delayed by up to 2 weeks. with the popularity of social media growing,the internet is a source for syndromic surveillance due to the availability of large amounts of data. in this study,tweets,or posts of 140 characters or less,from the website twitter were collected and analyzed for their potential as surveillance for seasonal influenza. objective: there were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ili) rates by city through filtering and a machine-learning classifier,(2) to observe correlations of tweets for emergency department ili rates by city,and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in san diego. methods: tweets containing the keyword flu were collected within a 17-mile radius from 11 us cities selected for population and availability of ili data. at the end of the collection period,159,802 tweets were used for correlation analyses with sentinel-provided ili and emergency department ili rates as reported by the corresponding city or county health department. two separate methods were used to observe correlations between tweets and ili rates: filtering the tweets by type (non-retweets,retweets,tweets with a url,tweets without a url),and the use of a machine-learning classifier that determined whether a tweet was valid
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کلیدواژه
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or from a user who was likely ill with the flu. Results: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. Conclusions: Compared to tweet analyses in the previous influenza season
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آدرس
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graduate school of public health,san diego state university,san diego,ca, United States, department of geography,san diego state university,storm hall 313c 5500 campanile drive,san diego,ca 92115, United States, school of communication,san diego state university,san diego,ca, United States, department of geography,san diego state university,storm hall 313c 5500 campanile drive,san diego,ca 92115, United States, department of linguistics,san diego state university,san diego,ca, United States, department of political science,san diego state university,san diego,ca, United States, graduate school of public health,san diego state university,san diego,ca, United States, graduate school of public health,san diego state university,san diego,ca, United States, department of geography,san diego state university,storm hall 313c 5500 campanile drive,san diego,ca 92115, United States, department of geography,san diego state university,storm hall 313c 5500 campanile drive,san diego,ca 92115, United States, graduate school of public health,san diego state university,san diego,ca, United States
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Authors
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