Can Twitter predict disease outbreaks?

City University, London, UK.
BMJ (online) (Impact Factor: 16.38). 05/2012; 344:e2353. DOI: 10.1136/bmj.e2353
Source: PubMed
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    ABSTRACT: Two fires along the eastern coast of Spain recently destroyed thousands of hectares of forest.1 We monitored information updates on the catastrophe, mainly via Twitter.In view of the often chaotic management of information.2 we have devised a road map for the media and public bodies to follow when using social networks to provide information on unfolding disasters. 1. Official public information needs to come from one distinct place, with a Twitter account. Every tweet (or message on Facebook) must include a link to that site to confirm the veracity of information.2. The emergencies 112 website should have a complete list of verified Twitter accounts by type of emergency, whether volunteers’ organisations, local government, or civil protection. It should also record alerts or incidents; give official emergency warnings; and provide volunteer related news, help, and relevant media reports.3. Information on road closures, access points to towns, and active fire points should be shown almost in real time on a Google Maps-type map.4. Because many users uploaded photos of the fire, the media should add information to these images regarding their exact location, time, and date. The images would then not be used to spread panic and distort reality.5. The media should avoid re-tweeting non-verified information.The basic principles of information in disasters and emergencies3—presence of verified information that is reliable and easy to consult—must be extrapolated to social networks, and should focus on spreading news quickly and denying hoaxes.4NotesCite this as: BMJ 2012;345:e4814
    BMJ (online) 07/2012; 345(jul23 2):e4814-e4814. DOI:10.1136/bmj.e4814 · 16.38 Impact Factor
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    ABSTRACT: Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions-especially anger-emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level. © The Author(s) 2015.
    Psychological Science 01/2015; 26(2). DOI:10.1177/0956797614557867 · 4.43 Impact Factor
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    ABSTRACT: During the last 30 years it has become commonplace for epidemiological studies to collect locational attributes of disease data. Although this advancement was driven largely by the introduction of handheld global positioning systems (GPS) and more recently, smartphones and tablets with built-in GPS, the collection of georeferenced disease data has moved beyond the use of handheld GPS devices and there now exist numerous sources of crowdsourced georeferenced disease data such as that available from georeferencing of Google search queries or Twitter messages. In addition, cartography has moved beyond the realm of professionals to crowdsourced mapping projects that play a crucial role in disease control and surveillance of outbreaks such as the 2014 West Africa Ebola epidemic. This paper provides a comprehensive review of a range of innovative sources of spatial animal and human health data including data warehouses, mHealth, Google Earth, volunteered geographic information and mining of internet-based big data sources such as Google and Twitter. We discuss the advantages, limitations and applications of each, and highlight studies where they have been used effectively.
    Spatial and Spatio-temporal Epidemiology 05/2015; DOI:10.1016/j.sste.2015.04.003