Tweet Stream Analysis Health 2.0 Meets Ix Conference Hashtag: #Health2Con Boston, April 22-23, 2009

Source and Acknowledgements Data pulled from HealthBirds.com at 8:45pm (Pacific) on April 26, 2009 ( http:// bit.ly/auRUC ) Healthbirds is the central nexus of everything Health & Twitter Thank you to Gilles Frydman (@gfry), founder of HealthBirds Thank you to Dave deBronkart (@ePatientDave) and Cindy Throop (@cindythroop) for initial analyses and inspiration Thanks to the Health 2.0 community on Twitter

Data pulled from HealthBirds.com at 8:45pm (Pacific) on April 26, 2009 ( http:// bit.ly/auRUC )

Healthbirds is the central nexus of everything Health & Twitter

Thank you to Gilles Frydman (@gfry), founder of HealthBirds

Thank you to Dave deBronkart (@ePatientDave) and Cindy Throop (@cindythroop) for initial analyses and inspiration

Thanks to the Health 2.0 community on Twitter

Summary statistics: we tweeted a lot What can we learn from the extensive content contributed to #health2con?

Thanks to all those who contributed to #Health2Con

Who was tweeting to #Health2Con? 344 individuals posted 3,388 tweets Long tail: 45% wrote 1 tweet; 70% wrote 5 or less tweets 18% wrote 10 or more tweets (62/344) 18.5% of users wrote 80% of content

344 individuals posted 3,388 tweets

Long tail: 45% wrote 1 tweet; 70% wrote 5 or less tweets

18% wrote 10 or more tweets (62/344)

18.5% of users wrote 80% of content

Top 50 most prolific posters to #Health2Con Top 50 individuals (15% of individuals) sent 2,545 tweets (75% of tweets sent) Top 50 each sent 15 or more tweets with an average of 51 tweets

Top 50 individuals (15% of individuals) sent 2,545 tweets (75% of tweets sent)

Top 50 each sent 15 or more tweets with an average of 51 tweets

But what can we learn from tweet stream analysis? Tweet stream analysis could be very powerful with the proper tools 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…

Tweet stream analysis could be very powerful with the proper tools

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…

Can we learn the community’s interests or priorities from most common words used? Health (738), Patient/Patients (443), Docs/Doctor (323) Is it a good sign that “patient” was the second most tweeted word? Use of “data” and “info/information” is good, but what words should be present or bigger? “community”? “design”? others?

Health (738), Patient/Patients (443), Docs/Doctor (323)

Is it a good sign that “patient” was the second most tweeted word?

Use of “data” and “info/information” is good, but what words should be present or bigger? “community”? “design”? others?

What can we learn from the way people use specific keywords or phrases? Many Eyes interactive version available at: http:// bit.ly/dCtEz

What can we learn about our Health 2.0 network? Can we map the Health 2.0 community on Twitter via conference tweet stream analysis? Note: Chart is not actual Health 2.0 network

Can we map the Health 2.0 community on Twitter via conference tweet stream analysis?

Can we identify individuals responsible for keeping the conversation going? Individuals who were most often sent @ replies Note: Only considered @ reply if @name was placed first in tweet

Individuals who were most often sent @ replies

Biggest “conversationalists” @ replies sent to person vs. @ replies sent by person @ePatientDave Many Eyes interactive version available at: http://bit.ly/RI0nR

@ replies sent to person vs. @ replies sent by person

Can we identify influencers within the network? Individuals whose tweets were most often re-tweeted Note: Only captures first RT per tweet

Individuals whose tweets were most often re-tweeted

Biggest “distributors” Re-tweets sent by person vs. Times a person was re-tweeted Due to volume of tweets or value of tweets? @ekivemark @Doctor_V Many Eyes interactive version available at: http://bit.ly/OWs1r

Re-tweets sent by person vs. Times a person was re-tweeted

Due to volume of tweets or value of tweets?

Timeline of tweets to #Health2Con Tweet volume shows clear delineation between sessions Grouped tweets in 15 minute intervals Wednesday April 22, 2009 Thursday April 23, 2009

Tweet volume shows clear delineation between sessions

Grouped tweets in 15 minute intervals

What can we learn about individual presentations? TagCloud from 2:00pm – 4:00pm on Thursday 4/23 Great Debate #5: "User-generated content vs. Expert": What's the best approach to Knowledge Creation? Denise Basow and Dan Hoch

TagCloud from 2:00pm – 4:00pm on Thursday 4/23

Great Debate #5: "User-generated content vs. Expert": What's the best approach to Knowledge Creation?

Denise Basow and Dan Hoch

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”

All questions. Few answers. Thanks for reading. What do you think we can learn from tweet stream analysis? Contact me with comments, questions or if you would like to receive the raw data file (.xls) [email_address] www.twitter.com/@cwhogg www.linkedin.com/in/cwhogg

What do you think we can learn from tweet stream analysis?

Contact me with comments, questions or if you would like to receive the raw data file (.xls)

[email_address]

www.twitter.com/@cwhogg

www.linkedin.com/in/cwhogg