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Detection of Influenza in its earliest stage would facilitate timely action that could reduce the spread of the illness. Existing systems such as CDC and EISS which try to collect diagnosis data, are almost entirely manual, resulting in about two-week delays for clinical data acquisition. Twitter, a popular microblogging service, provides us with a perfect source for early-stage flu detection due to its real- time nature. For example, when a flu breaks out, people that get the flu may post related tweets which enables the detection of the flu breakout promptly. In this paper, we investigate the real-time flu detection problem on Twitter data by proposing Flu Markov Network (Flu-MN): a spatio-temporal unsupervised Bayesian algorithm based on a 4 phase Markov Network, trying to identify the flu breakout at the earliest stage. We test our model on real Twitter datasets from the United States along with baselines in multiple applications, such as real-time flu breakout detection, future epidemic phase prediction, or Influenza-like illness (ILI) physician visits. Experimental results show the robustness and effectiveness of our approach. We build up a real time flu reporting system based on the proposed approach, and we are hopeful that it would help government or health organizations in identifying flu outbreaks and facilitating timely actions to decrease unnecessary mortality.", "publicationTitle": "arXiv:1309.7340 [cs]", "publisher": "", "place": "", "date": "2013-09-27", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://arxiv.org/abs/1309.7340", "accessDate": "2014-08-21T02:22:04Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "00009 \narXiv: 1309.7340", "tags": [ { "tag": "Computer Science - Computation and Language", "type": 1 }, { "tag": "Computer Science - Social and Information Networks", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2014-08-21T02:22:04Z", "dateModified": "2015-03-30T13:48:56Z" } }, { "key": "DK6ADG6N", "version": 10, "library": { "type": "group", "id": 273094, "name": "AlertaDengue", "links": { "alternate": { "href": "https://www.zotero.org/groups/alertadengue", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/273094/items/DK6ADG6N", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/alertadengue/items/DK6ADG6N", "type": "text/html" } }, "meta": { "createdByUser": { "id": 970601, "username": "joycefigueiro", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/joycefigueiro", "type": "text/html" } } }, "creatorSummary": "Broniatowski et al.", "parsedDate": "2013", "numChildren": 2 }, "data": { "key": "DK6ADG6N", "version": 10, "itemType": "journalArticle", "title": "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic", "creators": [ { "creatorType": "author", "firstName": "David A.", "lastName": "Broniatowski" }, { "creatorType": "author", "firstName": "Michael J.", "lastName": "Paul" }, { "creatorType": "author", "firstName": "Mark", "lastName": "Dredze" } ], "abstractNote": "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.", "publicationTitle": "PLoS ONE", "publisher": "", "place": "", "date": "Dezembro 9, 2013", "volume": "8", "issue": "12", "section": "", "partNumber": "", "partTitle": "", "pages": "e83672", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "PLoS ONE", "DOI": "10.1371/journal.pone.0083672", "citationKey": "", "url": "http://dx.doi.org/10.1371/journal.pone.0083672", "accessDate": "2014-08-21T02:21:16Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "National and Local Influenza Surveillance through Twitter", "language": "", "libraryCatalog": "PLoS Journals", "callNumber": "", "rights": "", "extra": "00026", "tags": [], "collections": [], "relations": {}, 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