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Leveraging Social Media for Influenza Surveillance: A Data Mining Approach

This research paper explores how social media data can be mined to monitor and predict influenza trends, offering valuable insights for disease surveillance. The study highlights the correlation between online chatter and real-world flu outbreaks, demonstrating the potential of web and social media as early warning systems.

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Leveraging Social Media for Influenza Surveillance: A Data Mining Approach

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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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