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

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Influenza A(H1N1)Executive Summary:Natural Language Processing of Twitter #swineflu Posts using the Semantic MEDLINE PrototypeDr. AllaKeselman, Dr. Thomas Rindflesch, David HaleNational Library of Medicine, National Institutes of Health,Department of Health and Human ServicesMay 2009

CDCemergency page on Twitter showing posts during the initial H1N1 outbreak, April 2009http://twitter.com/CDCemergency

H1N1 information via Twitter:Communication issuesInformation receiversInformation overload>12,000 #swineflu (H1N1) posts/hour @ peakSignal:Noise ratioQuality?Authority?Twitter accounts impersonating CDCInformation providersEffective information provisionBiosurveillance

(un)Controlled VocabularyFolksonomyHashtags (#)GrammarAbbreviationsSRSLY IMO ROI 4 RT? YMMVHigh context

Examples of #swineflu Tweets

Acquisition ChallengesTwitter timelineStorage requirementsPrivacyTwitter APILimited search functionalityTemporal and range limitationsRange definition limited to midnight1500 posts from limit

Semantic MEDLINE PrototypeSummarizes MEDLINE citations returned by PubMed searchNatural Language Processing (MetaMap, SemRep) used to analyze salient content in titles and abstractsInformation presented in graph that has links to the MEDLINE text processedVisualize relationships, such as:A is a process of BX treats Y

Semantic MEDLINE Prototype Search pageBreast Cancer is highlighted in a list of available MEDLINE searches.

Semantic MEDLINE Prototype Search pageSummarize page for term: breast cancer.“Malignant neoplasm of breast” is highlighted in a list of topics on which to summarize.

Semantic MEDLINE Prototype Search pageSemantic MEDLINE Visualization. The term “Malignant neoplasm of breast” is in the center of the page. Dark blue arrows point into the center showing that terms such as “Endocrine therapy” and “Operative Surgical Procedures” TREATS the center term. Medications such as “trastuzumab” and “Tamoxifen” point to the center term. A brown arrow points from the center to the term “human” showing that the center term is a PROCESS OF “human.”

Semantic processing of#swineflu TweetsSample - 1267 TweetsAfternoon of April 27, 2009No adjustments made to NLP software (MetaMap, SemRep)No additional vocabulary, abbreviations, etc.

Preliminary Processing of #swineflu TweetsSample page from SemRep report, showing SemRep processing, Concepts, and Filter concepts.

Preliminary Processing of #swineflu TweetsSample page from SemRep report, showing Filter concepts by semantic type, Predications, and PROCESS_OF terms.

Concepts in Tweets Isolated by Semantic ProcessingDisease: influenzaDisease symptom: coughingGeographic area: MexicoAnimal: family suidaeHealth care organization: Centers for Disease Control and Prevention (U.S.)Medical device: mask

Next StepsProcessing of larger datasetinclude non-H1N1-related TweetsAdditional vocabularyFolksonomy, abbreviations, etc.Visualization of semantic processing results

OpportunitiesBiosurveillanceMonitoring of wide-spread sentimentTargeted information provisionRespond to misinformation trendsEvaluation of accuracy/authenticity

LinksSemantic MEDLINE Prototypehttp://skr3.nlm.nih.gov/SemMedDemo/Semantic Medline: Multi-Document Summarization and Visualizationhttp://www.nlm.nih.gov/pubs/techbull/mj07/theater_ppt/semantic.pptNational Library of Medicinehttp://www.nlm.nih.govNational Institutes of Healthhttp://nih.govDepartment of Health and Human Serviceshttp://hhs.gov

Dr. AllaKeselmankeselmana AT mail DOT nlm DOT nih DOT govDr. Thomas Rindfleschtrindflesch AT mail DOT nih DOT govDavid Haledavid DOT hale ATnih DOT gov

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

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/
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 adjustments made to NLP software (MetaMap, SemRep) – No additional vocabulary, abbreviations, etc.
Preliminary Processing of #swineflu Tweets
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