Influenza A(H1N1)

Executive Summary:Natural Language Processing of Twitter

#swineflu Posts using the Semantic MEDLINE Prototype

Dr. Alla Keselman, Dr. Thomas Rindflesch, David HaleNational Library of Medicine, National Institutes of Health,

Department of Health and Human ServicesMay 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)Controlled Vocabulary

• 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 Isolatedby 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. Alla Keselmankeselmana AT mail DOT nlm DOT nih DOT gov

Dr. Thomas Rindfleschtrindflesch AT mail DOT nih DOT gov

David Haledavid DOT hale AT nih DOT gov