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Monitoring Influenza Trends though Mining Social Media

Monitoring Influenza Trends though Mining Social Media. By Courtney D Corley, Armin R Mikler , Karan P Singh, and Diane J Cook . Jedsada Chartree 02/07/2011. Outline. Introduction Motivation Methodology Results Conclusion. Introduction.

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Monitoring Influenza Trends though Mining Social Media

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  1. Monitoring Influenza Trends though Mining Social Media By Courtney D Corley, Armin R Mikler, Karan P Singh, and Diane J Cook Jedsada Chartree 02/07/2011

  2. Outline • Introduction • Motivation • Methodology • Results • Conclusion

  3. Introduction • 1. Influenza (Flu) is an infectious disease caused by influenza viruses, that affects birds and mammals. Source: http://en.wikipedia.org/wiki/Influenza

  4. Introduction • Influenza Symptoms - Chills, fever, sore throat, muscle pains, severe headache, coughing, weakness/fatigue • Influenza Transmission - Air (coughs/sneezes) - Direct contact Source: http://en.wikipedia.org/wiki/Influenza

  5. Introduction Influenza season in the US Source: http://www.google.org/flutrends/us/#US

  6. Introduction • 2. Social Media - Media for social interaction - The use of web-based and mobile technology to turn communication into interactive dialogue.

  7. Introduction Social Media: Blogger, WordPress, Google Buzz, Twitter, Facebook, Hi5, MySpace Source: http://www.webseoanalytics.com/blog/social-media-best-practices-for-businesses/

  8. Motivation • Difficulty of identifying the Influenza - Patients with Influenza-like-illness (ILI) have to be examined by physicians. • Web and Social Media (WSM) provide a resource increases in ILI.

  9. Methodology • Data - Spinn3r: a web service for indexing all blogs connected as community/social network . - 44 million posts from 1-August to 30-September, 2008

  10. Methodology/Results Actual and Average Blog-World Posts per Day of Week

  11. Methodology/Results

  12. Methodology/Results Autocorrelation Function (ACF) is the similarity between observations as a function of the time separation between them.

  13. Methodology/Results FC-post trends

  14. Methodology/Results Blog Category occurrence per Month

  15. Response Strategy in “Flu” Blog Communities • Identify WSM Influenza-related communities that share flu-postings which could disseminate information. - Bloggers: first response (link analysis) - Readers

  16. Response Strategy in “Flu” Blog Communities Closeness: Finding the average shortest parts from each actor and all reachable actors. Betweenness centrality: A blog is central if it lies between other blogs. Google’s PageRank: A numerical weighting to each website.

  17. Response Strategy in “Flu” Blog Communities

  18. Conclusion • Strong correlation between FC-Posts per week and CDC • Web and social media provide resources to detect increases in ILI • WSM Influenza-related communities could share information in the case of flu outbreak.

  19. References • C. Corley, A. Mikler, K. Singh, and D. Cook. 2009. Monitoring influenza trends through mining social media. International Conference on Bioinformatics and Computational Biology (BIOCOMP09).

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