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Health 2.0 Tweet Stream Analysis

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Tweet Stream Analysis of Tweets from Health 2.0 Conference in Boston, April 22-23, 2009.

Veröffentlicht in: Technologie, Gesundheit & Medizin

Health 2.0 Tweet Stream Analysis

  1. 1. Tweet Stream Analysis Health 2.0 Meets Ix Conference Hashtag: #Health2Con Boston, April 22-23, 2009
  2. 2. Source and Acknowledgements <ul><li>Data pulled from HealthBirds.com at 8:45pm (Pacific) on April 26, 2009 ( http:// bit.ly/auRUC ) </li></ul><ul><ul><li>Healthbirds is the central nexus of everything Health & Twitter </li></ul></ul><ul><li>Thank you to Gilles Frydman (@gfry), founder of HealthBirds </li></ul><ul><li>Thank you to Dave deBronkart (@ePatientDave) and Cindy Throop (@cindythroop) for initial analyses and inspiration </li></ul><ul><li>Thanks to the Health 2.0 community on Twitter </li></ul>
  3. 3. Summary statistics: we tweeted a lot What can we learn from the extensive content contributed to #health2con?
  4. 4. Thanks to all those who contributed to #Health2Con
  5. 5. Who was tweeting to #Health2Con? <ul><li>344 individuals posted 3,388 tweets </li></ul><ul><li>Long tail: 45% wrote 1 tweet; 70% wrote 5 or less tweets </li></ul><ul><li>18% wrote 10 or more tweets (62/344) </li></ul><ul><li>18.5% of users wrote 80% of content </li></ul>
  6. 6. Top 50 most prolific posters to #Health2Con <ul><li>Top 50 individuals (15% of individuals) sent 2,545 tweets (75% of tweets sent) </li></ul><ul><ul><li>Top 50 each sent 15 or more tweets with an average of 51 tweets </li></ul></ul>
  7. 7. But what can we learn from tweet stream analysis? <ul><li>Tweet stream analysis could be very powerful with the proper tools </li></ul><ul><li>Unfortunately, I don’t know what those tools are and don’t have the API to @mikekirkwood’s brain. So this deck only raises questions… </li></ul>
  8. 8. Can we learn the community’s interests or priorities from most common words used? <ul><li>Health (738), Patient/Patients (443), Docs/Doctor (323) </li></ul><ul><li>Is it a good sign that “patient” was the second most tweeted word? </li></ul><ul><li>Use of “data” and “info/information” is good, but what words should be present or bigger? “community”? “design”? others? </li></ul>
  9. 9. What can we learn from the way people use specific keywords or phrases? Many Eyes interactive version available at: http:// bit.ly/dCtEz
  10. 10. What can we learn about our Health 2.0 network? <ul><li>Can we map the Health 2.0 community on Twitter via conference tweet stream analysis? </li></ul>Note: Chart is not actual Health 2.0 network
  11. 11. Can we identify individuals responsible for keeping the conversation going? <ul><li>Individuals who were most often sent @ replies </li></ul>Note: Only considered @ reply if @name was placed first in tweet
  12. 12. Biggest “conversationalists” <ul><li>@ replies sent to person vs. @ replies sent by person </li></ul>@ePatientDave Many Eyes interactive version available at: http://bit.ly/RI0nR
  13. 13. Can we identify influencers within the network? <ul><li>Individuals whose tweets were most often re-tweeted </li></ul>Note: Only captures first RT per tweet
  14. 14. Biggest “distributors” <ul><li>Re-tweets sent by person vs. Times a person was re-tweeted </li></ul><ul><ul><li>Due to volume of tweets or value of tweets? </li></ul></ul>@ekivemark @Doctor_V Many Eyes interactive version available at: http://bit.ly/OWs1r
  15. 15. Timeline of tweets to #Health2Con <ul><li>Tweet volume shows clear delineation between sessions </li></ul><ul><ul><li>Grouped tweets in 15 minute intervals </li></ul></ul>Wednesday April 22, 2009 Thursday April 23, 2009
  16. 16. What can we learn about individual presentations? <ul><li>TagCloud from 2:00pm – 4:00pm on Thursday 4/23 </li></ul><ul><ul><li>Great Debate #5: &quot;User-generated content vs. Expert&quot;: What's the best approach to Knowledge Creation? </li></ul></ul><ul><ul><li>Denise Basow and Dan Hoch </li></ul></ul>
  17. 17. Can we learn sentiment or key interests about individual companies from short demos? Interactive version available at: http:// bit.ly/dCtEz Note: analysis of tweets including “curetogether” or “cure together”
  18. 18. All questions. Few answers. Thanks for reading. <ul><li>What do you think we can learn from tweet stream analysis? </li></ul><ul><li>Contact me with comments, questions or if you would like to receive the raw data file (.xls) </li></ul><ul><li>[email_address] </li></ul><ul><li>www.twitter.com/@cwhogg </li></ul><ul><li>www.linkedin.com/in/cwhogg </li></ul>