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

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-w...

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Detalles Bibliográficos
Autores principales: Corley, Courtney D., Cook, Diane J., Mikler, Armin R., Singh, Karan P.
Formato: Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2010
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872292/
https://www.ncbi.nlm.nih.gov/pubmed/20616993
http://dx.doi.org/10.3390/ijerph7020596
Descripción
Sumario: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.

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