1. Bringing Together the Social and Technical in Big Data Analytics: Why You Can't Predict the Flu from Twitter, and Here's How David A. Broniatowski Asst. Prof. EMSE http://www.seas.gwu.edu/~broniatowski

2. PUBLIC HEALTH CYCLE PopulationDoctors Surveillance Intervention

3. Traditional mechanisms Surveys Clinical visits REQUIRES: DATA ON THE POPULATION This has limited research

4. TWITTER Short messages (140 chars) posted to public internet Content: news, conversation, pointless babble Huge volume 500 million a day

5. WHY TWITTER? Huge volumes of data A constant stream of small updates Nothing like waiting in line to buy cigarettes behind a guy in a business suit buying gasoline with ten dollars in dimes I eat pizza too much I'm at Cvs Pharmacy (117th and kendall, Miami)

6. INFLUENZA SURVEILLANCE

7. CDC has nationwide surveillance network with 2700 outpatient centers reporting ILI: influenza-like illness Cons: Slow (2 weeks) Varying levels of geographic granularity

8. TWITTER SURVEILLANCE Twitter influenza surveillance must be 1) Accurately track ground truth Identify infection tweets 2) Effective at both municipal and national level Expand tweet geolocation and evaluate municipal accuracy 3) Predictive in real time Deploy previously trained system on this flu season

11. PIPELINE CLASSIFIERS Three steps using supervised machine learning+NLP Step 1: Identify health tweets Step 2: Identify flu related Step 3: Awareness vs. infection

12. TWITTER SURVEILLANCE Twitter influenza surveillance must be 1) Accurately track ground truth Identify infection tweets 2) Effective at both municipal and national level Expand tweet geolocation and evaluate municipal accuracy 3) Predictive in real time Deploy previously trained system on this flu season

13. LOCAL EFFECTIVENESS Current work focuses on US national flu rates Useful surveillance needed by region/state/city How can Twitter track local trends? Is it accurate? Is there enough data? Only about 1% of Twitter is geocoded

15. CARMEN (Dredze et al., 2013) Over 4000 known locations (countries, states, counties, cities) Geocordinates only: ~1% Expanded locations: ~22% Available in Python and Java

16. TWITTER SURVEILLANCE Twitter influenza surveillance must be 1) Accurately track ground truth Identify infection tweets 2) Effective at both municipal and national level Expand tweet geolocation and evaluate municipal accuracy 3) Predictive in real time Deploy previously trained system on this flu season

17. SURVEILLANCE RESULTS Pearson Correlation 20092011 Keywords0.970.646 Flu Classifier0.970.519 Google Flu Trends 0.970.897 Infection0.9720.7832

18. GOOGLE FLU TRENDS GETS IT WRONG? Lohr, S. (2014). Google flu trends: the limits of big data. New York Times.

19. Pearson Correlation: Keywords: 0.75 Infection: 0.93

21. ILI counts: Infection: 0.88 Keywords: 0.72 BLIND EVALUATION

22. 2013-2014 0.95 Correlation

24. MOST RECENT DATA Broniatowski, D. A., Dredze, M., Paul, M. J., & Dugas, A. (2015). Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study. JMIR Public Health and Surveillance, 1(1), e5.

25. PREDICTING ACTUAL FLU IN BALTIMORE Broniatowski, D. A., Dredze, M., Paul, M. J., & Dugas, A. (2015). Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study. JMIR Public Health and Surveillance, 1(1), e5.

26. HEALTHTWEETS.ORG

27. HEALTHTWEETS WORLDWIDE

28. Some Other Projects David A. Broniatowski Asst. Prof. EMSE http://www.seas.gwu.edu/~broniatowski

29. 29 BIG DATA FOR GROUP DECISION MAKING: EXTRACTING SOCIAL NETWORKS FROM FDA ADVISORY PANEL MEETING TRANSCRIPTS (Broniatowski & Magee, 2013 American Journal of Therapeutics; Broniatowski & Magee, 2012 IEEE Signal Processing Magazine; Broniatowski & Magee, in preparation)

30. “GERMS ARE GERMS” AND “WHY NOT TAKE A RISK?” MODELS AND DATA FOR RISKY DECISION MAKING IN THE ED (Broniatowski, Klein, & Reyna, in press, Medical Decision Making Broniatowski & Reyna, in preparation)

31. Examples: Phylogenetic trees General Motors Problem decomposition Tree HierarchyLayered Hierarchy Examples: Levels of abstraction Law firm organization Problem abstraction Grid Networks and Teams Examples: Contagion Markets Crowdsourcing Families (teams) HOW DO WE DESIGN SYSTEMS TO USE INFORMATION FLOW TO OUR ADVANTAGE? We would like to deepen our intuition regarding system architectures (Broniatowski & Moses, in preparation)

32. 32 QUESTIONS? Big data Influenza tracking and coupled contagion Group decision-making Individual decision-making Formal models Medical and engineering applications Formal and mathematical models Systems architecture Design for flexibility broniatowski@gwu.edu