CiteULike | Further Information

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Mendeley | Further Information

{"title"=>"National and local influenza surveillance through twitter: An analysis of the 2012-2013 influenza epidemic", "type"=>"journal", "authors"=>[{"first_name"=>"David A.", "last_name"=>"Broniatowski", "scopus_author_id"=>"15080660400"}, {"first_name"=>"Michael J.", "last_name"=>"Paul", "scopus_author_id"=>"7402403521"}, {"first_name"=>"Mark", "last_name"=>"Dredze", "scopus_author_id"=>"14041686400"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"372094578", "sgr"=>"84891941337", "pmid"=>"24349542", "scopus"=>"2-s2.0-84891941337", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0083672", "issn"=>"19326203"}, "id"=>"0d9bc649-2dd1-3d02-a7d1-c24772b4e6d8", "abstract"=>"Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases \"chatter\" - messages that are about influenza but that do not pertain to an actual infection - masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012-2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system's influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.", "link"=>"http://www.mendeley.com/research/national-local-influenza-surveillance-through-twitter-analysis-20122013-influenza-epidemic", "reader_count"=>185, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>28, "Student > Doctoral Student"=>5, "Student > Ph. D. 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• {"id"=>"448130542098055168", "text"=>"#PLOSONE: National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic http://t.co/wkQiQ0eyLN", "created_at"=>"2014-03-24T16:13:41Z", "user"=>"Phrygienne", "user_name"=>"Ann Séror", "user_profile_image"=>"http://pbs.twimg.com/profile_images/1520573555/Ann_S_ror_normal.jpg"}
• {"id"=>"453941845689511936", "text"=>"#PLOSONE: National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic http://t.co/th4IfE3jA6", "created_at"=>"2014-04-09T17:05:44Z", "user"=>"eusoj", "user_name"=>"Josué G. Gutiérrez", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3468917410/664b1db6ee632d524b147d527bf113d0_normal.jpeg"}
• {"id"=>"500630201299386368", "text"=>"T1 Flu surveillance through Twitter. http://t.co/ytXvQN6Lag published on PLOS #HealthXPH", "created_at"=>"2014-08-16T13:08:35Z", "user"=>"endocrine_witch", "user_name"=>"Iris Thiele Isip Tan", "user_profile_image"=>"http://pbs.twimg.com/profile_images/1030094453/3Ra_normal.jpg"}
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• {"id"=>"530622034100109312", "text"=>"@waltercurioso también les hablaré de este caso http://t.co/wguKWPDN2O @PLOSONE", "created_at"=>"2014-11-07T07:25:26Z", "user"=>"AniversarioPeru", "user_name"=>"AniversarioPeru", "user_profile_image"=>"http://pbs.twimg.com/profile_images/521968797666660352/l09M7y7K_normal.png"}
• {"id"=>"614022978930671616", "text"=>"Number of tweets about flu\nhttp://t.co/cic42imJ7H\nabout a week behing the conventional approach.\n#UKCCRF15 @ICNARCCTU @PLOSONE", "created_at"=>"2015-06-25T10:51:00Z", "user"=>"UKCCRF", "user_name"=>"UKCCRF", "user_profile_image"=>"http://pbs.twimg.com/profile_images/587835037543833601/z_PZjJYt_normal.png"}
• {"id"=>"614023689869389824", "text"=>"Influenza Surveillance through Twitter - big data in social media http://t.co/K9wEFBgaZs #UKCCRF15 http://t.co/Qa2pTWhJDA", "created_at"=>"2015-06-25T10:53:50Z", "user"=>"sj_shepherd", "user_name"=>"Stephen Shepherd", "user_profile_image"=>"http://pbs.twimg.com/profile_images/599586269606248448/mVrmLyyI_normal.png"}
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• {"id"=>"614024667427393536", "text"=>"RT @UKCCRF: Number of tweets about flu\nhttp://t.co/cic42imJ7H\nabout a week behing the conventional approach.\n#UKCCRF15 @ICNARCCTU @PLOSONE", "created_at"=>"2015-06-25T10:57:43Z", "user"=>"DarkNatter", "user_name"=>"Paul Dark", "user_profile_image"=>"http://pbs.twimg.com/profile_images/594539145684504576/sFfSL_ZU_normal.jpg"}
• {"id"=>"614331593986080768", "text"=>"#ECS2015 - forget Google searches... Twitter: An Analysis of the 2012-2013 Influenza Epidemic. http://t.co/sXzHRMUpbH", "created_at"=>"2015-06-26T07:17:20Z", "user"=>"HoratioNelson", "user_name"=>"Philippe Borremans", "user_profile_image"=>"http://pbs.twimg.com/profile_images/550264137792888833/T6x0OnK8_normal.jpeg"}
• {"id"=>"614902393503883264", "text"=>"RT @sj_shepherd: Influenza Surveillance through Twitter - big data in social media http://t.co/K9wEFBgaZs #UKCCRF15 http://t.co/Qa2pTWhJDA", "created_at"=>"2015-06-27T21:05:29Z", "user"=>"DarkNatter", "user_name"=>"Paul Dark", "user_profile_image"=>"http://pbs.twimg.com/profile_images/594539145684504576/sFfSL_ZU_normal.jpg"}
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• {"id"=>"615176261871271936", "text"=>"RT @sj_shepherd: Influenza Surveillance through Twitter - big data in social media http://t.co/K9wEFBgaZs #UKCCRF15 http://t.co/Qa2pTWhJDA", "created_at"=>"2015-06-28T15:13:44Z", "user"=>"MsNaughtyCheese", "user_name"=>"rebecca", "user_profile_image"=>"http://pbs.twimg.com/profile_images/598769840464203776/sAy3J4I-_normal.jpg"}
• {"id"=>"621748751829479424", "text"=>"PLOS ONE: National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic http://t.co/5a9lKfxKLI", "created_at"=>"2015-07-16T18:30:28Z", "user"=>"dongyue", "user_name"=>"Yue Dong, M.D.", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3543714506/40e1209f2db09da0628c878525f99c7a_normal.jpeg"}
• {"id"=>"688024595211907072", "text"=>"#PLOSONE: #Influenza Surveillance from 2012-2013 using #Twitter &amp; computer algorithm filter for #flu sx's reports https://t.co/RQfB9oSuOW", "created_at"=>"2016-01-15T15:47:01Z", "user"=>"hoque_michelle", "user_name"=>"Michelle Hoque", "user_profile_image"=>"http://pbs.twimg.com/profile_images/653355731756826624/uOtY0guW_normal.jpg"}
• {"id"=>"688034196909125633", "text"=>"RT @hoque_michelle: #PLOSONE: #Influenza Surveillance from 2012-2013 using #Twitter &amp; computer algorithm filter for #flu sx's reports htt…", "created_at"=>"2016-01-15T16:25:10Z", "user"=>"smwilson45", "user_name"=>"Steven Wilson", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3767829720/1f65adde4ef1fe4ee3b85a27a0a0d77e_normal.jpeg"}

Wikipedia

Wordpress.com | Further Information

Counter

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1307985"], "description"=>"<p>This figure shows the cross-correlation function for the residuals of the second-order-differenced CDC and Twitter infection data, as described in the “Time Series Analysis” section. These results show that the Twitter estimates neither lead nor lag the CDC ILI rates, although the Twitter data are publicly available up to two weeks earlier than CDC data.</p>", "links"=>[], "tags"=>["twitter", "rates", "cdc", "ili"], "article_id"=>872021, "categories"=>["Biological Sciences"], "users"=>["David A. Broniatowski", "Michael J. Paul", "Mark Dredze"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083672.g003", "stats"=>{"downloads"=>1, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_correlation_between_Twitter_infection_rates_and_CDC_ILI_rates_/872021", "title"=>"Cross-correlation between Twitter infection rates and CDC ILI rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:16:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1307983"], "description"=>"<p>This figure shows the components of our system for estimating influenza prevalence from Twitter. A stream of tweets matching hundreds of health-related keywords is passed through three classification filters to remove irrelevant tweets. The locations of tweets are identified with our geolocation system, Carmen, and only tweets in the location of interest are saved. The volume of tweets is normalized by the total volume of tweets from a random sample of Twitter to produce a prevalence measure.</p>", "links"=>[], "tags"=>["estimating", "influenza", "prevalence"], "article_id"=>872019, "categories"=>["Biological Sciences"], "users"=>["David A. Broniatowski", "Michael J. Paul", "Mark Dredze"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083672.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_System_for_estimating_influenza_prevalence_from_Twitter_/872019", "title"=>"System for estimating influenza prevalence from Twitter.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:16:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1307984"], "description"=>"<p>This figure shows national influenza rates of the United States as predicted by two Twitter-based algorithms alongside the influenza-like illness surveillance network data from the US Centers for Disease Control and Prevention (CDC). The dashed blue line is the measure estimated by a simple model of keyword matching, while the solid blue line is the measure estimated by our new infection detection model. Our new algorithm more closely matches the CDC data (solid black line), while the simpler keyword model infers spurious spikes due to other Twitter chatter, e.g. in early December and early April.</p>", "links"=>[], "tags"=>["influenza", "rates", "twitter", "cdc"], "article_id"=>872020, "categories"=>["Biological Sciences"], "users"=>["David A. Broniatowski", "Michael J. Paul", "Mark Dredze"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0083672.g002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_2012_2013_national_influenza_rates_from_Twitter_and_CDC_surveillance_/872020", "title"=>"2012-2013 national influenza rates from Twitter and CDC surveillance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-09 03:16:37"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime