Text and Structural Data Mining of Influenza Mentions in Web and Social Media

Courtney D. Corley, Diane J. Cook, Armin R. Mikler and Karan P. Singh
Additional contact information
Courtney D. Corley: Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
Diane J. Cook: School of Electrical Engineering and Computer Science, Washington State University, PO Box 642752 Pullman, Washington 99164, USA
Armin R. Mikler: Department of Computer Science and Engineering, University of North Texas, 1155 Union Circle #311366 Denton, TX 76203, USA
Karan P. Singh: Department of Biostatistics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd. Fort Worth, TX 76107, USA

IJERPH, 2010, vol. 7, issue 2, 1-20

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.

Keywords: disease surveillance; public health epidemiology; health informatics; graph-based data mining; web and social media; social network analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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