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Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak

Publication Date: November 29, 2010
Journal: PLOS ONE
Authors: Cynthia Chew & Gunther Eysenbach
Volume: 5
Issue: 11
Pages: e14118
DOI: https://dx.plos.org/10.1371/journal.pone.0014118
Publisher URL: http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0014118
PubMed: http://www.ncbi.nlm.nih.gov/pubmed/21124761
PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2993925
Europe PMC: http://europepmc.org/abstract/MED/21124761
Web of Science: 000284686500008
Scopus: 78649725192
Mendeley: http://www.mendeley.com/research/sars-revisited-managing-outbreaks-communications

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{"title"=>"SARS revisited: Managing \"outbreaks\" with \" communications\"", "type"=>"generic", "authors"=>[{"first_name"=>"K. U.", "last_name"=>"Menon", "scopus_author_id"=>"14030447100"}], "year"=>2006, "source"=>"Annals of the Academy of Medicine Singapore", "identifiers"=>{"pui"=>"43974157", "arxiv"=>"NIHMS150003", "issn"=>"03044602", "isbn"=>"0304-4602", "doi"=>"10.1371/journal.pone.0014118", "scopus"=>"2-s2.0-33745666477", "pmid"=>"16830005", "sgr"=>"33745666477"}, "keywords"=>["Confidence building", "Risk", "Technological aids", "Transparency", "Trust"], "id"=>"a0d707fb-f153-3a8a-8fc0-ff2128fa32b7", "abstract"=>"\"Risk communications\" has acquired some importance in the wake of our experience of SARS. Handled well, it helps to build mutual respect between a government or an organisation and the target groups with which it is communicating. It helps nurture public trust and confidence in getting over the crisis. The World Health Organization (WHO) has also come to recognise its importance after SARS and organised the first Expert Consultation on Outbreak Communications conference in Singapore in September 2004. This article assesses the context and the key features which worked to Singapore's advantage. Looking at the data now widely available on the Internet of the experience of SARS-infected countries like China, Taiwan, Canada, the article identifies the key areas of strategic communications in which Singapore fared particularly well. Another issue discussed is whether Singapore's experience has universal applicability or whether it is limited because of Singapore's unique cultural, historical and geographical circumstances. 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{"referral"=>"http://blogs.plos.org/everyone/?p=7405", "language"=>"English", "title"=>"Tweet for the sake of science | EveryONE: The PLOS ONE blog", "type"=>"Blog", "publication"=>"EveryONE", "published_on"=>"", "link_state"=>"APPROVED", "id"=>72428, "created"=>"2017-11-07T06:58:36.000Z", "crowdsourced"=>false, "article"=>{"id"=>13963, "doi"=>"10.1371/journal.pone.0014118", "title"=>"Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak", "published_on"=>"2010-11-29T00:00:00.000Z"}}

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Journal Comments 121 Oct 13:02 UTC

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{"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/f835bba6-7909-469e-ba0f-95ea051a571d", "title"=>"So what?", "body"=>"I have little problem with your methodology, indeed what you did regarding automated examination of tweets might be of interest in computational and machine-analysis circles. However I don't see why your report is of any interest regarding medicine. For one thing, similar observations were made years ago regarding the use of Google trends to detect public interest in medical events. Yet, you don't reference these studies. Worse, your study lacks geo-information (which Google Trends provides) and so is even closer to useless. People also generate huge tweet volumes regarding Ashton Kucher, so what?\n\nI did find one of the graphs to be of interest. Figure 4 - Scatterplot of tweets sharing personal experiences and USA H1N1 incidence rate.- as it seems to further back up my critique. The graph shows that out of the 2M tweets recorded, only a few tens of thousands were identified as related to personal experience with H1N1. That is not a high ratio ( a few %). Additionally of note, the actual incidence of H1N1 was about half the # of tweets. Indeed the virus was much more of a scare than it was an epidemic. In that regard, what did we learn from social media? Only that peeps love to relay reports that the sky is falling.... Some things never change....", "isRemoved"=>false, "created"=>"2011-01-25T17:42:58Z", "lastModified"=>"2011-01-25T17:42:58Z", "creator"=>{"userId"=>"8581"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0014118", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>2, "mostRecentActivity"=>"2014-09-29T10:26:52Z", "replies"=>[{"type"=>"REPLY", "parentID"=>21841, "annotationUri"=>"info:doi/10.1371/reply/2953c9c3-e00f-497c-b80c-d31001fddfb2", "title"=>"RE: So what?", "body"=>"In response to ronewolf:\n1. What is the \"interest regarding medicine\": See http://www.cbc.ca/health/story/2011/01/17/health-web-social-media.html - Quote: \"When public health officials talk about social media and using social media, what they mean is basically getting our messages out to the public but that's not what the social web is all about. It is only half the equation. The other half is also using this to learn from what people are talking about on the social web and using that information to customize and tailor your own public health messages.\"\nOut intention is to develop tool/methods allowing public health professionals to do that.\n\n2. \"using Google trends to detect public interest not referenced\": Not sure what particular study you are referring to, but for all I know ref 6 (my own study, initiated in 2003 and published in 2006) was the first study suggesting to use Google searches for public health purposes (this was before Google trends became available, and many years before Google Flutrends). \nFurther references are in the bibliography of ref 7.\nTo our knowledge, this is the first study using tweets. Search data are \"sparse\" data, whereas tweets are much richer and allow a distinction of the users' intention, behavior, knowledge, attitude, attention to topics.\n\n3. No geocoding: Although not done in this study, geocoding is possible and we are using it in other projects. ", "isRemoved"=>false, "created"=>"2011-01-25T21:45:50Z", "lastModified"=>"2011-01-25T21:45:50Z", "creator"=>{"userId"=>"4811"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0014118", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>1, "mostRecentActivity"=>"2014-09-29T10:26:52Z", "replies"=>[{"type"=>"REPLY", "parentID"=>34903, "annotationUri"=>"info:doi/10.1371/reply/0214f1b8-f956-4096-b987-2a90fc3987e4", "title"=>"RE: RE: So what?", "body"=>"Looking forward to see other projects that incorporate geocoding, I believe it's a very important point when it comes to studies that take place on Google.It's not as easy to do with Twitter as a recent study showed most users do not add a real location in their profile, and location must be inferred from other factors.\n\nHans Jehann\nhttp://www.egga-referencement.ch/solutions.php", "isRemoved"=>false, "created"=>"2014-09-29T10:26:52Z", "lastModified"=>"2014-09-29T10:26:52Z", "creator"=>{"userId"=>"465067"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0014118", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2014-09-29T10:26:52Z", "replies"=>[]}]}]}

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Figshare 19201 Apr 13:51 UTC

Figshare

{"files"=>["https://ndownloader.figshare.com/files/816272"], "description"=>"Figure was scaled to the highest peak on Nov. 27 where 1.33% of all tweets expressed relief. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement. 1 = July 5: Harry Potter actor Rupert Grint has H1N1. 2 = Nov 25–27: #iamthankfulfor campaign.", "links"=>[], "tags"=>["tweets", "expressing", "december"], "article_id"=>486645, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g010", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_expressing_relief_from_May_1_to_December_31_2009_/486645", "title"=>"The relative proportion of tweets expressing relief from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:50:45"}
{"files"=>["https://ndownloader.figshare.com/files/816876"], "description"=>"Descriptions and Examples of Qualifier Categories.", "links"=>[], "tags"=>["examples", "qualifier"], "article_id"=>487249, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.t002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptions_and_Examples_of_Qualifier_Categories_/487249", "title"=>"Descriptions and Examples of Qualifier Categories.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 02:00:49"}
{"files"=>["https://ndownloader.figshare.com/files/816634"], "description"=>"Figure was scaled to the highest peak on Dec. 25 where 77.27% of all tweets shared resources. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement.", "links"=>[], "tags"=>["tweets", "sharing", "resources", "december"], "article_id"=>487010, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g015", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_sharing_resources_from_May_1_to_December_31_2009_/487010", "title"=>"The relative proportion of tweets sharing resources from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:56:50"}
{"files"=>["https://ndownloader.figshare.com/files/816566"], "description"=>"Figure was scaled to the highest peak on July 5 where 18.99% of all tweets shared questions. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement. 1 = July 5: Harry Potter actor Rupert Grint has H1N1. 2 = July 20: Viral dissemination of the “face mask (H1N1) versus condom (AIDS) comparison.” 3 = August 5: Unexplained drop in questions.", "links"=>[], "tags"=>["tweets", "sharing", "questions", "december"], "article_id"=>486937, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g014", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_sharing_questions_from_May_1_to_December_31_2009_/486937", "title"=>"The relative proportion of tweets sharing questions from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:55:37"}
{"files"=>["https://ndownloader.figshare.com/files/816035"], "description"=>"Figure was scaled to the highest peak on May 1 where 4.65% of all tweets expressed frustration. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement.", "links"=>[], "tags"=>["tweets", "expressing", "december"], "article_id"=>486405, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g007", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_expressing_frustration_from_May_1_to_December_31_2009_/486405", "title"=>"The relative proportion of tweets expressing frustration from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:46:45"}
{"files"=>["https://ndownloader.figshare.com/files/816735"], "description"=>"a2300 hrs on Sept. 28 had only 20 eligible tweets.bThe percent total will not equal 100% as not all tweets had qualifiers and tweets with multiple qualifiers were coded multiple times. Approximately 40% of tweets contained qualifiers.cThe total will not equal to 100% as not all tweets had links. Approximately 60% of tweets contained links.*chi-square test for trend significant at p<0.05.", "links"=>[], "tags"=>["links", "manually", "coded", "h1n1"], "article_id"=>487104, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.t004", "stats"=>{"downloads"=>11, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Content_Qualifiers_and_Links_of_Manually_Coded_H1N1_Tweets_/487104", "title"=>"Content, Qualifiers, and Links of Manually Coded H1N1 Tweets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 01:58:24"}
{"files"=>["https://ndownloader.figshare.com/files/815521"], "description"=>"Lines = absolute number. Bars = relative percentage. Blue = “swine flu” or swineflu. Red = H1N1. Green = (“swine flu” or swineflu) AND H1N1.", "links"=>[], "tags"=>["containing", "swine", "december"], "article_id"=>485896, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g001", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tweets_containing_H1N1_swine_flu_or_both_from_May_to_December_2009_/485896", "title"=>"Tweets containing H1N1, swine flu, or both from May to December 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:38:16"}
{"files"=>["https://ndownloader.figshare.com/files/816705"], "description"=>"Descriptions and Examples of Automated Coding Search Patterns.", "links"=>[], "tags"=>["examples", "automated", "coding"], "article_id"=>487074, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.t005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptions_and_Examples_of_Automated_Coding_Search_Patterns_/487074", "title"=>"Descriptions and Examples of Automated Coding Search Patterns.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 01:57:54"}
{"files"=>["https://ndownloader.figshare.com/files/815791"], "description"=>"Scatterplot of tweets sharing personal experiences and USA H1N1 incidence rate.", "links"=>[], "tags"=>["tweets", "sharing", "experiences", "usa", "h1n1", "incidence"], "article_id"=>486158, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatterplot_of_tweets_sharing_personal_experiences_and_USA_H1N1_incidence_rate_/486158", "title"=>"Scatterplot of tweets sharing personal experiences and USA H1N1 incidence rate.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:42:38"}
{"files"=>["https://ndownloader.figshare.com/files/816850"], "description"=>"Descriptions and Examples of Link Categories.", "links"=>[], "tags"=>["examples"], "article_id"=>487228, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.t003", "stats"=>{"downloads"=>6, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptions_and_Examples_of_Link_Categories_/487228", "title"=>"Descriptions and Examples of Link Categories.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 02:00:28"}
{"files"=>["https://ndownloader.figshare.com/files/816344"], "description"=>"Figure was scaled to the highest peak on Sept. 20 where 1.93% of all tweets expressed misinformation. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement. 1 = Aug 2: CBS reports on parental concerns about H1N1. 2 = Sept 18–21: Ten swine flu lies told by the mainstream media. 3 = Nov 27: WHO and drug companies in collusion. 4 = Dec 25: Carbon monoxide poisoning can create same symptoms as H1N1.", "links"=>[], "tags"=>["tweets", "expressing", "misinformation", "december"], "article_id"=>486713, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g011", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_expressing_misinformation_from_May_1_to_December_31_2009_/486713", "title"=>"The relative proportion of tweets expressing misinformation from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:51:53"}
{"files"=>["https://ndownloader.figshare.com/files/815640"], "description"=>"Non-linear pattern of tweeted misinformation identified via manual coding from May to December 2009.", "links"=>[], "tags"=>["tweeted", "misinformation", "coding", "december"], "article_id"=>486007, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g002", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Non_linear_pattern_of_tweeted_misinformation_identified_via_manual_coding_from_May_to_December_2009_/486007", "title"=>"Non-linear pattern of tweeted misinformation identified via manual coding from May to December 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:40:07"}
{"files"=>["https://ndownloader.figshare.com/files/816176"], "description"=>"Figure was scaled to the highest peak on July 5 where 30.66% of all tweets expressed concern. The peak was assigned a score of 100. Subconcepts: Blue = concern for others. Red = concern for self. Yellow = concerned emoticons. Green = general concern. A = June 11: WHO pandemic level 6 announcement. 1 = July 5: Harry Potter actor Rupert Grint has H1N1.", "links"=>[], "tags"=>["tweets", "expressing", "december"], "article_id"=>486552, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g009", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_expressing_concern_from_May_1_to_December_31_2009_/486552", "title"=>"The relative proportion of tweets expressing concern from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:49:12"}
{"files"=>["https://ndownloader.figshare.com/files/815944"], "description"=>"Figure was scaled to the highest peak on July 5 where 9.82% of all tweets expressed misinformation. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement. 1 = July 5: Harry Potter actor Rupert Grint has H1N1.", "links"=>[], "tags"=>["tweets", "expressing", "humour", "december"], "article_id"=>486309, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g006", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_expressing_humour_from_May_1_to_December_31_2009_/486309", "title"=>"The relative proportion of tweets expressing humour from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:45:09"}
{"files"=>["https://ndownloader.figshare.com/files/816819"], "description"=>"Descriptions and Examples of Content Categories.", "links"=>[], "tags"=>["examples"], "article_id"=>487193, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptions_and_Examples_of_Content_Categories_/487193", "title"=>"Descriptions and Examples of Content Categories.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 01:59:53"}
{"files"=>["https://ndownloader.figshare.com/files/816111"], "description"=>"Figure was scaled to the highest peak on Oct. 20 where 5.64% of all tweets shared personal experiences. The peak was assigned a score of 100. Subconcepts: Red = indirect (family/friend) experience. Yellow = personal/direct experience. Blue = vaccination experience. A = June 11: WHO pandemic level 6 announcement. 1 = Oct 6: H1N1 vacctionations arrive in the US.", "links"=>[], "tags"=>["tweets", "sharing", "experiences", "december"], "article_id"=>486482, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g008", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_sharing_personal_experiences_from_May_1_to_December_31_2009_/486482", "title"=>"The relative proportion of tweets sharing personal experiences from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:48:02"}
{"files"=>["https://ndownloader.figshare.com/files/815706"], "description"=>"Example scatterplots of manual versus automated coding proportions.", "links"=>[], "tags"=>["scatterplots", "automated", "coding"], "article_id"=>486075, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g003", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_scatterplots_of_manual_versus_automated_coding_proportions_/486075", "title"=>"Example scatterplots of manual versus automated coding proportions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:41:15"}
{"files"=>["https://ndownloader.figshare.com/files/816481"], "description"=>"Figure was scaled to the highest peak on Sept. 8 where 5.34% of all tweets shared personal opinions. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement. 1 = Sept. 8: Case of H1N1 confirmed at PAX videogame convention in Seattle.", "links"=>[], "tags"=>["tweets", "sharing", "opinions", "december"], "article_id"=>486856, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g013", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_sharing_personal_opinions_amp_interest_from_May_1_to_December_31_2009_/486856", "title"=>"The relative proportion of tweets sharing personal opinions & interest from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:54:16"}
{"files"=>["https://ndownloader.figshare.com/files/815877"], "description"=>"Scatterplot of tweets expressing concern and USA H1N1 incidence rate.", "links"=>[], "tags"=>["tweets", "expressing", "usa", "h1n1", "incidence"], "article_id"=>486242, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatterplot_of_tweets_expressing_concern_and_USA_H1N1_incidence_rate_/486242", "title"=>"Scatterplot of tweets expressing concern and USA H1N1 incidence rate.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:44:02"}
{"files"=>["https://ndownloader.figshare.com/files/816791"], "description"=>"N = 9.*significant at p<0.05.", "links"=>[], "tags"=>["automated"], "article_id"=>487164, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.t006", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_between_Manual_and_Automated_Coding_/487164", "title"=>"Correlations between Manual and Automated Coding.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 01:59:24"}
{"files"=>["https://ndownloader.figshare.com/files/406729"], "description"=>"<div><h3>Background</h3>Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary “infoveillance” approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms “H1N1” versus “swine flu” over time; 2) conduct a content analysis of “tweets”; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool.<h3>Methodology/Principal Findings</h3>Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords “swine flu,” “swineflu,” and/or “H1N1.” using Infovigil, an infoveillance system. Tweets using “H1N1” increased from 8.8% to 40.5% (R2 = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%). 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data.<h3>Conclusions</h3>This study illustrates the potential of using social media to conduct “infodemiology” studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.</div>", "links"=>[], "tags"=>["pandemics", "tweets", "2009", "h1n1", "outbreak"], "article_id"=>140376, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118", "stats"=>{"downloads"=>18, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Pandemics_in_the_Age_of_Twitter_Content_Analysis_of_Tweets_during_the_2009_H1N1_Outbreak/140376", "title"=>"Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 00:06:16"}
{"files"=>["https://ndownloader.figshare.com/files/816769"], "description"=>"*chi-square test for trend significant at p<0.05.", "links"=>[], "tags"=>["coding", "trends"], "article_id"=>487141, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.t007", "stats"=>{"downloads"=>5, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automated_Coding_Trends_over_Time_/487141", "title"=>"Automated Coding Trends over Time.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-11-29 01:59:01"}
{"files"=>["https://ndownloader.figshare.com/files/816415"], "description"=>"Figure was scaled to the highest peak on July 20 where 6.96% of all tweets expressed downplayed risk. The peak was assigned a score of 100. A = June 11: WHO pandemic level 6 announcement. 1 = July 20: Viral dissemination of the “face mask (H1N1) versus condom (AIDS) comparison.” 2 = Dec 1: Viral dissemination of the “face mask (H1N1) versus condom (AIDS) comparison.”", "links"=>[], "tags"=>["tweets", "expressing", "downplayed", "december"], "article_id"=>486783, "categories"=>["Medicine", "Biotechnology"], "users"=>["Cynthia Chew", "Gunther Eysenbach"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014118.g012", "stats"=>{"downloads"=>4, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relative_proportion_of_tweets_expressing_downplayed_risk_from_May_1_to_December_31_2009_/486783", "title"=>"The relative proportion of tweets expressing downplayed risk from May 1 to December 31, 2009.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-11-29 01:53:03"}

PMC Usage Stats 788917 Jan 15:28 UTC

PMC Usage Stats | Further Information

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Relative Metric 79643405 Apr 17:36 UTC

Relative Metric

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F1000Prime20 Jan 12:25 UTC

F1000Prime

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