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Twitter: A Good Place to Detect Health Conditions

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      Abstract

      With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain.

      We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society.

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      Most cited references 6

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      Detecting influenza epidemics using search engine query data.

      Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
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        The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic

        Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's “tweets,” or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.
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          Dissemination of health information through social networks: twitter and antibiotics.

          This study reviewed Twitter status updates mentioning "antibiotic(s)" to determine overarching categories and explore evidence of misunderstanding or misuse of antibiotics. One thousand Twitter status updates mentioning antibiotic(s) were randomly selected for content analysis and categorization. To explore cases of potential misunderstanding or misuse, these status updates were mined for co-occurrence of the following terms: "cold + antibiotic(s)," "extra + antibiotic(s)," "flu + antibiotic(s)," "leftover + antibiotic(s)," and "share + antibiotic(s)" and reviewed to confirm evidence of misuse or misunderstanding. Of the 1000 status updates, 971 were categorized into 11 groups: general use (n = 289), advice/information (n = 157), side effects/negative reactions (n = 113), diagnosis (n = 102), resistance (n = 92), misunderstanding and/or misuse (n = 55), positive reactions (n = 48), animals (n = 46), other (n = 42), wanting/needing (n = 19), and cost (n = 8). Cases of misunderstanding or abuse were identified for the following combinations: "flu + antibiotic(s)" (n = 345), "cold + antibiotic(s)" (n = 302), "leftover + antibiotic(s)" (n = 23), "share + antibiotic(s)" (n = 10), and "extra + antibiotic(s)" (n = 7). Social media sites offer means of health information sharing. Further study is warranted to explore how such networks may provide a venue to identify misuse or misunderstanding of antibiotics, promote positive behavior change, disseminate valid information, and explore how such tools can be used to gather real-time health data. 2010 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.
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            Author and article information

            Affiliations
            [1 ]Department of Information and Communication Technologies, Campus Elviña s/n, Coruña, Spain
            [2 ]University of Aveiro, DETI/IEETA, Campus Universitariá de Santiago, Aveiro, Portugal
            University of Oxford, United Kingdom
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: VMP SM JLO. Performed the experiments: VMP. Analyzed the data: VMP SM MÁ FC JLO. Wrote the paper: VMP SM MÁ FC JLO.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2014
            29 January 2014
            : 9
            : 1
            3906034 PONE-D-13-10567 10.1371/journal.pone.0086191

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Pages: 11
            Funding
            The work of VMP, MA and FC was supported by Xunta de Galicia CN2012/211, the Ministry of Education and Science of Spain and FEDER funds of the European Union (Project TIN2009-14203). SM and JLO were funded by FEDER through the COMPETE programme and by Portuguese national funds through FCT - “Fundação Para a Ciência e a Tecnologia” under project number PTDC/EIA-CCO/100541/2008 (FCOMP-01-0124-FEDER-010029), and by the QREN Mais Centro program through the Cloud Thinking project (CENTRO-07-ST24-FEDER-002031). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Computer Science
            Algorithms
            Computer Applications
            Web-Based Applications
            Computing Methods
            Computer Inferencing
            Natural Language Processing
            Software Engineering
            Software Tools
            Text Mining
            Medicine
            Clinical Research Design
            Epidemiology
            Survey Research
            Epidemiology
            Epidemiological Methods
            Social Epidemiology
            Public Health
            Behavioral and Social Aspects of Health

            Uncategorized

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