In our previous post we looked at the growth of patient-centric conversations on Twitter visually identified in our healthcare dataset as green nodes. This time, we’re going to zoom in on one of those green nodes to take a closer look at the dynamic nature of these communities.
What you will see in the video below is a dynamic network centrality analysis of the conversations within the #rheum community during the month of August this year. During the first few seconds you will see the whole month’s conversations all together, giving you a quick glimpse of how vast, complex and beautiful these communities are.
After this glimpse you will see all the conversations dynamically over time as they happen. In the video, one second represents about six hours. A pink node represents a member of this community as they are participating in the conversations or are being pulled into the conversations by being referenced or mentioned. The larger the node, the more central or influential that member is to this community. A green link between two pink nodes signifies a conversation, a direct interaction between the two as they happen. The thicker the link, the stronger the conversation or interaction is between them.
One community, many conversations – learning from network centrality analysis
Like many other real-life communities, there can be many member and participants, but in any given time period, some are more central than others. You may call them influencers, instigators, perhaps even thought-leaders, or they just happen to be part of the topic for the day.
Several of these people can be identified in this visual, and one of the main individuals from this time-period is clearly @rawarrior (Kelly Young). Kelly is an amazing person, an epatient, and a moderator of the wonderful Rheumatoid Arthritis / Rheumatoid Autoimmune Disease weekly #rheum tweet chat.
Another familiar name shows up with a great intensity but only for a short time. As it happened, @mayoclinic (Mayo Clinic) published results from a research study relevant to the #rheum community during this month. From the network centrality analysis you can see how @mayoclinic is pulled into the conversation by a large number of people. Interestingly, we can observe that those same people are not the “regulars” of the #rheum community. A simple cluster analysis would probably identify several sub-cultures within this community, the “mayo”-group being one of them.
Creating meaning and structure within the noise
We raised the question in our previous post: why would patients want to use Twitter – a social network devoid of privacy? Although the network centrality analysis recorded in this video is pure math and numbers, we can still observe how dynamic, organic and real the interactions are in this community.
Of all the various patient community options online, does Twitter have certain attributes that better resemble some real-world communities? One could perhaps suggest that many are attracted by the “real-timeness” of Twitter as a publishing and communication platform (Book<Journal<Article<Blog<Tweet). We could also suggest that Twitter has strong “organic” features. Twitter is in one sense an unstructured madness. But just like in the chaos of real life, people create meaning and structure within the noise. Rules are few and are made up as you go. Communities are created in a spur of the moment. Some last, others fade away.
What more can we learn about the members of these communities? In our next post, we will look closer at the participants of one of these patient-centric Twitter communities by attempting to reduce some of the complexity in this network centrality analysis with further segmentation.
Update: The third post in this series is now published – The Flattening of Healthcare: Breaking Down Barriers in Healthcare Social Media
77 Responses to “The Dynamics of a Twitter Patient Community – Network Centrality Analysis”
Thomas M. Lee
The Dynamics of a Twitter Patient Community – Network Centrality Analysis http://t.co/weM6iOmv #hcsm #epatient
Robert West PhD
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Robert Green
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Emily Nicholas
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Symplur
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Al
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Health Consumers NSW
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RAW
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Eric Rumsey
Network Analysis of A Twitter Patient Community – #Rheum & @rawarrior, by @audvin http://t.co/ZRQoBQdx – MT @westr
GHLF
Network Analysis of A Twitter Patient Community – #Rheum & @rawarrior, by @audvin http://t.co/ZRQoBQdx – MT @westr
Shannon Ragland
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Mike Thompson, MDPhD
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Mike Thompson, MDPhD
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Mike Thompson, MDPhD
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Mike Thompson, MDPhD
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Mike Thompson, MDPhD
'…Twitter has strong “organic” features. Twitter is in one sense an unstructured madness.' – @Audvin via @Symplur http://t.co/H1mfKz2Y
Mike Thompson, MDPhD
'…Twitter has strong “organic” features. Twitter is in one sense an unstructured madness.' – @Audvin via @Symplur http://t.co/H1mfKz2Y
Mike Thompson, MDPhD
"Twitter…unstructured madness. But just like in chaos of real life, people create meaning &structure w/in the noise" http://t.co/uTCio1bm
Mike Thompson, MDPhD
"Twitter…unstructured madness. But just like in chaos of real life, people create meaning &structure w/in the noise" http://t.co/uTCio1bm
Mike Thompson, MDPhD
"Rules are few & are made up as you go. Communities are created in a spur of the moment. Some last, others fade away." http://t.co/DObuH2lf
CFHI-FCASS
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AMIA
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MS Hospital Assoc.
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Dr. Aman Khanna
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LeAnna J. Carey
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Mike Thompson, MDPhD
The Dynamics of a Twitter Patient Community – Network Centrality Analysis via @symplur http://t.co/inA5DYtp
Mike Thompson, MDPhD
'…Twitter has strong “organic” features. Twitter is in one sense an unstructured madness.' – @audvin via @symplur http://t.co/CSuiewR0
Mike Thompson, MDPhD
"Twitter…unstructured madness. But just like in chaos of real life, people create meaning &structure w/in the noise" http://t.co/kL1P6yfO
Mike Thompson, MDPhD
"Rules are few & are made up as you go. Communities are created in a spur of the moment. Some last, others fade away." http://t.co/61yDaNji
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DKennerson
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Mike Thompson, MDPhD
The Dynamics of a Twitter Patient Community – Network Centrality Analysis via @symplur http://t.co/inA5DYtp
Mike Thompson, MDPhD
'…Twitter has strong “organic” features. Twitter is in one sense an unstructured madness.' – @audvin via @symplur http://t.co/h8fRvXYD
Mike Thompson, MDPhD
"Twitter…unstructured madness. But just like in chaos of real life, people create meaning &structure w/in the noise" http://t.co/JK9wjmr9
Mike Thompson, MDPhD
"Rules are few & are made up as you go. Communities are created in a spur of the moment. Some last, others fade away." http://t.co/8lPlY6WY
Mian Hassan Nawaz
"Rules are few & are made up as you go. Communities are created in a spur of the moment. Some last, others fade away." http://t.co/8lPlY6WY
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Kerrie Noonan
RT @nashbeth: RT @symplur: The dynamics of a Twitter Patient Community – Network Centrality Analysis http://t.co/njTN2FLY #hcsm #hcsmca…
Kelly Young
A short video on how patient communities communicate. Featuring our #rheum community!! Each second is 6 hrs real… http://t.co/2SDBA0fe
E Neevs
A short video on how patient communities communicate. Featuring our #rheum community!! Each second is 6 hrs real… http://t.co/2SDBA0fe
Tracy
A short video on how patient communities communicate. Featuring our #rheum community!! Each second is 6 hrs real… http://t.co/2SDBA0fe
Emily Goss
A short video on how patient communities communicate. Featuring our #rheum community!! Each second is 6 hrs real… http://t.co/2SDBA0fe
Ingalill Atala
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Vangelis Sakkalis
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Dr. Dewey Jones IV
The dynamics of a Twitter Patient Community http://t.co/KyoOHOsr
Gina Reneé
A short video on how patient communities communicate. Featuring our #rheum community!! Each second is 6 hrs real… http://t.co/2SDBA0fe
MDWebPro
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Audun Utengen
What does a Tweet Chat look like? (data visualization) http://t.co/S9PeBXEk #hcsm #rheum #hcsmeu
Tony A. Hart
Best #hcsm Tweet Today | MT @audvin: What does a Tweet Chat look like? (data visual) http://t.co/X1L7j1xL #hcsmeu
Kelly Young
RT @audvin: What does a Tweet Chat look like? (data visualization) http://t.co/5X2nhhlF #hcsm #rheum #hcsmeu
Alexandra Fulford
fascinating RT @rawarrior: RT @audvin: What does a Tweet Chat look like? (data visualization) http://t.co/Xfc1uOIQ #hcsm #rheum #hcsmeu
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Screwpile Comm.
Cool visual of Twitter conversaions. | The dynamics of a Twitter Patient Community – Network Centrality Analysis http://t.co/HrBblQhn
Michael
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Joseph Babaian
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Mike Thompson, MDPhD
#Healthcare Twitter communities: "Creating meaning and structure within the noise" http://t.co/b1dMnPwG v @mtmdphd #hcsm #hcsmca
Brian S. McGowan PhD
"@m_fkf: #Healthcare Twitter communities: "Creating meaning and structure within the noise" http://t.co/YyO8wjMP v @mtmdphd " #socialQI
Michelle Lemme
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Eugenio Santoro
The dynamics of a Twitter Patient Community – Network Centrality Analysis http://t.co/VLFJBeYA via @symplur
Jim Rawson MD
Dynamics of Twitter #Patient Community http://t.co/t3vT8vHk One person can make a difference. Nice job @rawarrior #HCSM #socialmedia
Garry Choy MD
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OttawaCoP
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Monika Wysocki
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Kelly Young
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Therese Humphrey
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Audun Utengen
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David Thompson
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