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

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

http://twitter.com/CDCemergency

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

(un)ControlledVocabulary • Folksonomy • Hashtags(#) • Grammar • Abbreviations – SRSLY IMO ROI 4 RT? YMMV • High context

#swineflu Tweets

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

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

http://skr3.nlm.nih.gov/SemMedDemo/

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.

Preliminary Processing of #swineflu Tweets

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

Next Steps • Processing of larger dataset – include non-H1N1-related Tweets • Additional vocabulary – Folksonomy, abbreviations, etc. • Visualization of semantic processing results

Opportunities • Biosurveillance • Monitoring of wide-spread sentiment • Targeted information provision – Respond to misinformation trends • Evaluation of accuracy/authenticity

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

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

by U.S. National Library of Medicine on Jun 02, 2009

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Executive Summary: Natural Language Processing of Twitter #swineflu (H1N1) Posts using Semantic MEDLINE Prototype — Presentation Transcript