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

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 on: Mar 3, 2016
Published in: Health & Medicine      Technology      
Source: www.slideshare.net


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

  • 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. http://twitter.com/CDCemergency
  • 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. (un)ControlledVocabulary • Folksonomy • Hashtags(#) • Grammar • Abbreviations – SRSLY IMO ROI 4 RT? YMMV • High context
  • 5. #swineflu Tweets
  • 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. 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. http://skr3.nlm.nih.gov/SemMedDemo/
  • 9. http://skr3.nlm.nih.gov/SemMedDemo/
  • 10. http://skr3.nlm.nih.gov/SemMedDemo/
  • 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. Preliminary Processing of #swineflu Tweets
  • 13. Preliminary Processing of #swineflu Tweets
  • 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. Next Steps • Processing of larger dataset – include non-H1N1-related Tweets • Additional vocabulary – Folksonomy, abbreviations, etc. • Visualization of semantic processing results
  • 16. Opportunities • Biosurveillance • Monitoring of wide-spread sentiment • Targeted information provision – Respond to misinformation trends • Evaluation of accuracy/authenticity
  • 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. 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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