Text and Structural Data Mining of Influenza Mentions in Web and Social MediaReport as inadecuate




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1

Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA

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School of Electrical Engineering and Computer Science, Washington State University, PO Box 642752 Pullman, Washington 99164, USA

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Department of Computer Science and Engineering, University of North Texas, 1155 Union Circle #311366 Denton, TX 76203, USA

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Department of Biostatistics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd. Fort Worth, TX 76107, USA





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Author to whom correspondence should be addressed.



Abstract Text and structural data mining of web and social media WSM provides a novel disease surveillance resource and can identify online communities for targeted public health communications PHC to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5 October 2008 to 21 March 2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags. View Full-Text

Keywords: disease surveillance; public health epidemiology; health informatics; graph-based data mining; web and social media; social network analysis disease surveillance; public health epidemiology; health informatics; graph-based data mining; web and social media; social network analysis





Author: Courtney D. Corley 1,* , Diane J. Cook 2, Armin R. Mikler 3 and Karan P. Singh 4

Source: http://mdpi.com/



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