Influenza Pattern Analysis System through Mining Weblogs
Weblogs are resource of social structure to discover and track the various type of information written by blogger. In this paper, we proposed to use mining weblogs technique for identifying the trends of influenza where blogger had disseminated their opinion for the anomaly disease. In order to identify the trends, web crawler is applied to perform a search and generated a list of visited links based on a set of influenza keywords. This information is used to implement the analytics report system for monitoring and analyzing the pattern and trends of influenza (H1N1). Statistical and graphical analysis reports are generated. Both types of the report have shown satisfactory reports that reflect the awareness of Malaysian on the issue of influenza outbreak through blogs.
[1] John S. Brownstein, Ph.D., Clark C. Freifeld, B.S., and Lawrence C.
Madoff, M.D. (2009) Digital Disease Detection ÔÇö Harnessing the Web
for Public Health Surveillance. The New England Journal of Medicine;
med 360;21, published at NEJM.org on May 7, 2009.
[2] J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski,
and L. Brilliant, "Detecting influenza epidemics using search engine
query data," Nature, vol. 457, no. 7232, pp. 1012-4, Feb 2009.
[3] Il-Chul Moon, Young-Min Kim, Hyun-Jong Lee, Alice H. Oh (2009)
Temporal Issue Trend Identifications in Blogs, published in Proceedings
of the CSE- 09 International Conference on Computational Science and
Engineering Volume-04.
[4] Courtney D Corley, Armin R Mikler, Karan P Singh and Diane J Cook
(2010) Monitoring Influenza Trends through Mining Social Media.
Int.J.Environ.Res.Public Health published in 22 February 2010.
[5] Richard Zhou (2008). Develop A Demographic Data Analysis Report
System By SAS® and Visual Basic, proceedings of the NESUG 2008.
[6] http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_
phase6_20090611/en/
[7] Nicola Marsden-Haug, Virginia B. Foster, Philip L. Gould, Eugene
Elbert Hailiang Wang, and Julie A. Pavlin (2007) Code-based
Syndromic Surveillance for Influenzalike Illness by International
Classification of Diseases, Ninth Revision published in Vol. 13, No. 2,
February 2007.
[8] Aron Culotta (2010) Towards detecting influenza epidemics by
analyzing Twitter messages. In 1st Workshop on social media Analytics
(SOMA-10), July 25, 2010, Washington, DC, USA, published in ACM
978-1-14503-0217-3.
[9] E. de Quincey and P. Kostkova (2009). Early warning and outbreak
detection using social networking websites: the potential of twitter,
electronic healthcare. In eHeath 2nd International Conference, Instanbul,
Tirkey, September 2009.
[10] J. Ritterman, M.Osborne, and E. Klein. Using prediction markets and
Twitter to predict a swine flu pandemic. In 1st International Workshop on
Mining Social Media, 2009.
[11] http://www.economist.com/node/15660874
[12] Julie Malida "The Changing Face of Health Care Fraud DetectionÔÇö
Predictive Analytics" BNA-s Health Care Fraud Report, Vol. 14, No. 4,
Feb. 23, 2011.
[13] http://www.newprosoft.com/web-spider.htm
[14] http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_
phase6_20090611/en/index.ht ml
[15] http://en.wikipedia.org/wiki/2009_flu_pandemic
[16] Michael Eberhart, (2008) MPH, Philadelphia Department of Public
Health. Make the Map You Want with PROC GMAP and the Annotate
Facility, proceedings of the NESUG 2008.
[17] Darrell Massengill and Jeff Phillips (2010), SAS Institute Inc., Cary,
NC. Tips and Tricks: More SAS/GRAPH® Map Secrets, proceedings of
the Paper 134-2010.
[18] Barbara B. Okerson, Virginia Health Quality Center, Glen Allen, VA.
Using SAS/GRAPH® GMAP to Enhance a Diabetes Wellness
Campaign, proceedings of the Paper 171-30.
[1] John S. Brownstein, Ph.D., Clark C. Freifeld, B.S., and Lawrence C.
Madoff, M.D. (2009) Digital Disease Detection ÔÇö Harnessing the Web
for Public Health Surveillance. The New England Journal of Medicine;
med 360;21, published at NEJM.org on May 7, 2009.
[2] J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski,
and L. Brilliant, "Detecting influenza epidemics using search engine
query data," Nature, vol. 457, no. 7232, pp. 1012-4, Feb 2009.
[3] Il-Chul Moon, Young-Min Kim, Hyun-Jong Lee, Alice H. Oh (2009)
Temporal Issue Trend Identifications in Blogs, published in Proceedings
of the CSE- 09 International Conference on Computational Science and
Engineering Volume-04.
[4] Courtney D Corley, Armin R Mikler, Karan P Singh and Diane J Cook
(2010) Monitoring Influenza Trends through Mining Social Media.
Int.J.Environ.Res.Public Health published in 22 February 2010.
[5] Richard Zhou (2008). Develop A Demographic Data Analysis Report
System By SAS® and Visual Basic, proceedings of the NESUG 2008.
[6] http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_
phase6_20090611/en/
[7] Nicola Marsden-Haug, Virginia B. Foster, Philip L. Gould, Eugene
Elbert Hailiang Wang, and Julie A. Pavlin (2007) Code-based
Syndromic Surveillance for Influenzalike Illness by International
Classification of Diseases, Ninth Revision published in Vol. 13, No. 2,
February 2007.
[8] Aron Culotta (2010) Towards detecting influenza epidemics by
analyzing Twitter messages. In 1st Workshop on social media Analytics
(SOMA-10), July 25, 2010, Washington, DC, USA, published in ACM
978-1-14503-0217-3.
[9] E. de Quincey and P. Kostkova (2009). Early warning and outbreak
detection using social networking websites: the potential of twitter,
electronic healthcare. In eHeath 2nd International Conference, Instanbul,
Tirkey, September 2009.
[10] J. Ritterman, M.Osborne, and E. Klein. Using prediction markets and
Twitter to predict a swine flu pandemic. In 1st International Workshop on
Mining Social Media, 2009.
[11] http://www.economist.com/node/15660874
[12] Julie Malida "The Changing Face of Health Care Fraud DetectionÔÇö
Predictive Analytics" BNA-s Health Care Fraud Report, Vol. 14, No. 4,
Feb. 23, 2011.
[13] http://www.newprosoft.com/web-spider.htm
[14] http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_
phase6_20090611/en/index.ht ml
[15] http://en.wikipedia.org/wiki/2009_flu_pandemic
[16] Michael Eberhart, (2008) MPH, Philadelphia Department of Public
Health. Make the Map You Want with PROC GMAP and the Annotate
Facility, proceedings of the NESUG 2008.
[17] Darrell Massengill and Jeff Phillips (2010), SAS Institute Inc., Cary,
NC. Tips and Tricks: More SAS/GRAPH® Map Secrets, proceedings of
the Paper 134-2010.
[18] Barbara B. Okerson, Virginia Health Quality Center, Glen Allen, VA.
Using SAS/GRAPH® GMAP to Enhance a Diabetes Wellness
Campaign, proceedings of the Paper 171-30.
@article{"International Journal of Information, Control and Computer Sciences:60589", author = "Pei Lin Khoo and Yunli Lee", title = "Influenza Pattern Analysis System through Mining Weblogs", abstract = "Weblogs are resource of social structure to discover and track the various type of information written by blogger. In this paper, we proposed to use mining weblogs technique for identifying the trends of influenza where blogger had disseminated their opinion for the anomaly disease. In order to identify the trends, web crawler is applied to perform a search and generated a list of visited links based on a set of influenza keywords. This information is used to implement the analytics report system for monitoring and analyzing the pattern and trends of influenza (H1N1). Statistical and graphical analysis reports are generated. Both types of the report have shown satisfactory reports that reflect the awareness of Malaysian on the issue of influenza outbreak through blogs.
", keywords = "H1N1, Weblogs, Web Crawler, Analytics Report
System.", volume = "6", number = "12", pages = "1707-6", }
