Text and Structural Data Mining of Influenza Mentions in Web and Social Media
International Journal of Environmental Research and Public HealthPublished 22 February 2010Open access
Courtney D. Corley, Diane J. Cook, Armin R. Mikler, Karan P. Singh
Citations239
SJR quartileQ2
SJR score0.92
SNIP1.22
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TL;DR
Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like illness patient report data and a graph-based data mining technique is brought to bear to detect anomalies among flu blogs connected by publisher type, links, and user-tags.
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
Social SciencesMedicinePhysics and Astronomy
Social network analysis methods and applications
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