"These metrics are intended to serve as an indicator of potential emerging health issues to spur further investigation and collection of direct measures of disease. In addition , recent studies demonstrate the value of combining social media with routine epidemiological data to detect or predict disease outbreaks, including influenza and cholera (Chew and Eysenbach, 2010; St Louis and Zorlu, 2012; Broniatowski et al., 2013; Chunara et al., 2012; Abrams et al., 2013) and to estimate weekly levels of influenza-like illness (Signorini et al., 2011). Although one of the main advantages of crowd-sourced tracking surveillance systems is that of timeliness through the availability of real-time, georeferenced data (Stoové and Pedrana, 2014), a major limitation is the large amount of unrelated 'noise' (Chew and Eysenbach, 2010; Broniatowski et al., 2013; Denecke et al., 2013), although Broniatowski et al. (2013) appear to have developed an algorithm that can successfully distinguish relevant tweets from noise. "
"As we described earlier, others have found social media data potentially useful to predict disease outbreaks when used in concert with other routine data (Broniatowski et al., 2013; Chew and Eysenbach, 2010; Chunara et al., 2012; St Louis and Zorlu, 2012). These experiences highlight potential utility for combining HIV prevalence and Twitter data as an 'early warning' system. "
[Show abstract][Hide abstract] ABSTRACT: This paper outlines a commentary response to an article published by Young and colleagues in Preventive Medicine that evaluated the feasibility of using Twitter as a surveillance and monitoring took for HIV. We draw upon the broader literature on disease surveillance and public health prevention using social media and broader considerations of epidemiological and surveillance methods to provide readers with necessary considerations for using social media in epidemiology and surveillance.
Preventive Medicine 06/2014; 63. DOI:10.1016/j.ypmed.2014.03.008 · 3.09 Impact Factor
"The service's extensive use, both in the United States as well as globally, creates many opportunities to augment decision support systems with Twitter-driven predictive analytics. Recent research has shown that tweets can be used to predict various large-scale events like elections , infectious disease outbreaks , and national revolutions . The essential hypothesis is that the location, timing, and content of tweets are informative with regard to future events. "
[Show abstract][Hide abstract] ABSTRACT: Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitter-driven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction. We use Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States. We then incorporate these topics into a crime prediction model and show that, for 19 of the 25 crime types we studied, the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation. We identify a number of performance bottlenecks that could impact the use of Twitter in an actual decision support system. We also point out important areas of future work for this research, including deeper semantic analysis of message content, temporal modeling, and incorporation of auxiliary data sources. This research has implications specifically for criminal justice decision makers in charge of resource allocation for crime prevention. More generally, this research has implications for decision makers concerned with geographic spaces occupied by Twitter-using individuals.
Decision Support Systems 05/2014; 61(1). DOI:10.1016/j.dss.2014.02.003 · 2.31 Impact Factor
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