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Influenza A(H1N1)
         Executive Summary:
    Natural Language Processing of
   Twitter #swineflu Posts using the
    ...
http://twitter.com/CDCemergency
H1N1 information via Twitter:
         Communication issues
• Information receivers
  – Information overload
     • >12,00...
(un)ControlledVocabulary
•   Folksonomy
•   Hashtags(#)
•   Grammar
•   Abbreviations
    – SRSLY IMO ROI 4 RT? YMMV
• Hig...
#swineflu Tweets
Acquisition Challenges
• Twitter timeline
  – Storage requirements
  – Privacy
• Twitter API
  – Limited search functional...
Semantic MEDLINE Prototype
• Summarizes MEDLINE citations returned by
  PubMed search
• Natural Language Processing
  (Met...
http://skr3.nlm.nih.gov/SemMedDemo/
http://skr3.nlm.nih.gov/SemMedDemo/
http://skr3.nlm.nih.gov/SemMedDemo/
Semantic processing of
            #swineflu Tweets
• Sample - 1267 Tweets
  – Afternoon of April 27, 2009
• No adjustment...
Preliminary Processing of #swineflu Tweets
Preliminary Processing of #swineflu Tweets
Concepts in Tweets Isolated
         by Semantic Processing
• Disease: influenza
• Disease symptom: coughing
• Geographic ...
Next Steps
• Processing of larger dataset
  – include non-H1N1-related Tweets
• Additional vocabulary
  – Folksonomy, abbr...
Opportunities
• Biosurveillance
• Monitoring of wide-spread sentiment
• Targeted information provision
  – Respond to misi...
Links
• Semantic MEDLINE Prototype
   – http://skr3.nlm.nih.gov/SemMedDemo/
• Semantic Medline: Multi-Document Summarizati...
Dr. AllaKeselman
keselmana AT mail DOT nlm DOT nih DOT gov

Dr. Thomas Rindflesch
trindflesch AT mail DOT nih DOT gov

Dav...
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Executive Summary: Natural Language Processing of Twitter #swineflu (H1N1) Posts using Semantic MEDLINE Prototype

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Natural Language Processing of Twitter #swineflu Posts using the Semantic MEDLINE Prototype at the National Library of Medicine, National Institutes of Health, U.S. Dept. of Health and Human Services

Published in: Health & Medicine, Technology
  • A norwegian blog about the swine flu can be found here; http://svineinfluensaen.blogspot.com
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Executive Summary: Natural Language Processing of Twitter #swineflu (H1N1) Posts using Semantic MEDLINE Prototype

  1. 1. Influenza A(H1N1) Executive Summary: Natural Language Processing of Twitter #swineflu Posts using the Semantic MEDLINE Prototype Dr. AllaKeselman, Dr. Thomas Rindflesch, David Hale National Library of Medicine, National Institutes of Health, Department of Health and Human Services May 2009
  2. 2. http://twitter.com/CDCemergency
  3. 3. H1N1 information via Twitter: Communication issues • Information receivers – Information overload • >12,000 #swineflu (H1N1) posts/hour @ peak – Signal:Noise ratio • Quality? • Authority? – Twitter accounts impersonating CDC • Information providers – Effective information provision – Biosurveillance
  4. 4. (un)ControlledVocabulary • Folksonomy • Hashtags(#) • Grammar • Abbreviations – SRSLY IMO ROI 4 RT? YMMV • High context
  5. 5. #swineflu Tweets
  6. 6. Acquisition Challenges • Twitter timeline – Storage requirements – Privacy • Twitter API – Limited search functionality • Temporal and range limitations – Range definition limited to midnight – 1500 posts from limit
  7. 7. Semantic MEDLINE Prototype • Summarizes MEDLINE citations returned by PubMed search • Natural Language Processing (MetaMap, SemRep) used to analyze salient content in titles and abstracts • Information presented in graph that has links to the MEDLINE text processed • Visualize relationships, such as: – A is a process of B – X treats Y
  8. 8. http://skr3.nlm.nih.gov/SemMedDemo/
  9. 9. http://skr3.nlm.nih.gov/SemMedDemo/
  10. 10. http://skr3.nlm.nih.gov/SemMedDemo/
  11. 11. Semantic processing of #swineflu Tweets • Sample - 1267 Tweets – Afternoon of April 27, 2009 • No adjustments made to NLP software (MetaMap, SemRep) – No additional vocabulary, abbreviations, etc.
  12. 12. Preliminary Processing of #swineflu Tweets
  13. 13. Preliminary Processing of #swineflu Tweets
  14. 14. Concepts in Tweets Isolated by Semantic Processing • Disease: influenza • Disease symptom: coughing • Geographic area: Mexico • Animal: family suidae • Health care organization: Centers for Disease Control and Prevention (U.S.) • Medical device: mask
  15. 15. Next Steps • Processing of larger dataset – include non-H1N1-related Tweets • Additional vocabulary – Folksonomy, abbreviations, etc. • Visualization of semantic processing results
  16. 16. Opportunities • Biosurveillance • Monitoring of wide-spread sentiment • Targeted information provision – Respond to misinformation trends • Evaluation of accuracy/authenticity
  17. 17. Links • Semantic MEDLINE Prototype – http://skr3.nlm.nih.gov/SemMedDemo/ • Semantic Medline: Multi-Document Summarization and Visualization – http://www.nlm.nih.gov/pubs/techbull/mj07/theater_ppt/ semantic.ppt • National Library of Medicine – http://www.nlm.nih.gov • National Institutes of Health – http://nih.gov • Department of Health and Human Services – http://hhs.gov
  18. 18. Dr. AllaKeselman keselmana AT mail DOT nlm DOT nih DOT gov Dr. Thomas Rindflesch trindflesch AT mail DOT nih DOT gov David Hale davidDOT hale AT nih DOT gov

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