Tweet Stream AnalysisHealth 2.0 Meets Ix ConferenceHashtag: #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

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

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1 344Individuals

# of

Tw

eets

Top 50 most prolific posters to #Health2Con

0102030405060708090

100110120130140150160170180

# of

Tw

eets

@ekivem

ark@

Doctor_V

@eP

atientDave

@healthblaw

g@

DrG

wenn

@shw

en@

john_chilmark

@healthythinker

@carlosrizo

@healthw

orldweb

@1sam

adams

@D

iabetesMine

@TrishaTorrey

@m

odulist@

MeredithG

ould@

htpotter@

IxCat

@doctorblogs

@am

biernacki@

cindythroop@

ChristineK

raft@

swisshealth20

@enochchoi

@2healthguru

@healthfinder

@davidgolub

@cw

hogg@

HealthLeaders

@C

ascadia@

designVoice

@dianelofgren

@B

PB

MD

2@

SusannahFox

@jenm

ccabegorma

@nancyshute

@jsonin

@organizedw

isdom@

anordine@

edshin@

jbeaudoin@

cdistefano@

PhilB

aumann

@ravisohal

@roopaonline

@m

obilehealth@

stellesmith

@P

rofkane@

drtonyah@

Anaisa

@bacigalupe

`

• 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…

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?

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 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

Biggest “conversationalists”

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# of @Replies to Person

# of

@R

eplie

s Se

nt B

y Pe

rson

@ePatientDave

@1samadams @DrGwenn@Doctor_V @ambiernacki@shwen @ekivemark@cindythroop @healthblawg@MeredithGould @IxCat@carlosrizo @Cascadia@TrishaTorrey

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

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# of Times Person Was Re-Tweeted

# of

Tim

es P

erso

n Se

nt a

Re-

Twee

tBiggest “distributors”

@ekivemark@Doctor_V

@john_chilmark @shwen@healthblawg @healthworldweb@ePatientDave @healthythinker@TrishaTorrey @DiabetesMine@MeredithGould @modulist@carlosrizo @DrGwenn

• Re-tweets sent by person vs. Times a person was re-tweeted– Due to volume of tweets or value of tweets?

Many Eyes interactive version available at: http://bit.ly/OWs1r

Apr 22 1,434 Tweets

(42%)Apr 23

1,602 Tweets (47%)

Apr 19-21 86 Tweets

(3%)

Apr 24-26 259 Tweets

(8%)

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

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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

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)

cwhogg@gmail.comwww.twitter.com/@cwhogg

www.linkedin.com/in/cwhogg