The Rise of Patient Communities on Twitter – Twitter Visualized

For over 2 years, Symplur has collected health conversations on Twitter. From a humble start, our infrastructure has grown and matured to scale with the tremendous growth of healthcare social media. We’re now approaching 150 million health tweets in our database which we analyze and categorize by thousands of variables. And similarly with other repositories of big data, our greatest challenge is to figure out how to present insights from such a vast dataset in a meaningful way. The answer is often found in visualizations.

We plan to start a small series of short posts displaying some of this data in a visual way.

What you see in the video below is a 22-month timeline of about 2,000 different health communities and topics each visualized as a bubble. In total, about 100 million healthcare tweets are represented in this visual. The green dots symbolize patient-centric topics, while pink encompasses more professional/provider topics. Larger bubbles signify larger volume of conversations within that community. The data is visualized dynamically over the this 22-month time period starting September 2010.

 

 

Why the growth of Patient Communities on Twitter?

What we discovered was somewhat of a surprise. From the start, many considered Twitter as a kind of virtual water cooler, mostly used by healthcare professionals. It’s been thought that the need for privacy would push patients to more closed platforms. Twitter as you know, is totally public and should not be considered private.

However, from what you can clearly observe, the green bubbles have grown in numbers and significance quite dramatically in past months. This indicates a strong growth of conversations in existing patient communities on Twitter and also a growth of new patient communities on Twitter within our dataset.

Why this growth? Why would patients choose to utilize a communication platform without privacy? We will continue this visual journey trying to give possible answers to that question.

 

Update: The second post in this series is now published –  The Dynamics of a Twitter Patient Community – Network Centrality Analysis

Audun Utengen - @audvin

Sharing research from the Healthcare Social Graph®. Co-founder @Symplur, Partner @RealChemistry_ ❤️ #Parkinsons #Schizophrenia #PancreaticCancer

173 Responses to “The Rise of Patient Communities on Twitter – Twitter Visualized”

  1. Jodi Sperber

    Great visualization. Thank you! I am not particularly surprised that patients are using this as a means of communication, even though it is not a private space. People are looking to connect, and the public space allows this to happen more organically. I also have a feeling that ease of use has something to do with it – there is little to learn aside from the platform specific lingo (@, #, RT, etc) and once that hurdle is crossed it is fairly simple to participate.

    • Audun Utengen, MBA

      Thank you Jodi.

      I’m in complete agreement with you, especially regarding the “organic” attribute. I will talk to that important point in the next post in this series.

      I’ve also been in the belief that one of the major attractions with Twitter for patients is exactly that it is without privacy! With no privacy comes complete transparency. With transparency comes discoverability. And since 1 in 5 patients are looking for people online with same condition (PEW), discoverability wins!

      There is a case to be made for Twitter and there is a case to be made for closed anonymous forums. Both serve a distinct job-to-be-done that will not disappear regardless of whatever technology will come and go.

  2. Robert West PhD

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  3. Kati Debelic

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  4. Audun Utengen

    Thanks :) RT @westr: Nice! RT @symplur: The Rise of Patient Communities on Twitter – Twitter Visualized http://t.co/QrDseYOg #hcsm #pm101

  5. Joe Capizzi

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  6. Cherise/LADA

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  7. Audun Utengen

    Thanks for you comment @jsperber :) The Rise of Patient Communities on Twitter – Twitter Visualized http://t.co/Ye8Z0ORr #hcsm #hcsmanz

  8. Lisa Fields

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  9. Ali R. Jalali

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  10. Colleen Young

    Interesting visualization Audvin. However, I question the separation of patient and provider communities. Where for example does the #hcsm and its geo-chapters fit in? I, for one, do not consider #hcsmca to be either patient- nor provider-centric. Our mission is to dismantle such silos and I believe my fellow #hcsm-chapter founders make the same effort. There are several other chats in which I participate (i.e., #hpm, #eolchat and even #bcsm) where I would be hard pressed to define as patient or provider.

    The beauty of Twitter is the intersection and confluence of people of all stripes. While I appreciate what this data demonstrates, I look forward to the day that we no longer need to make such a distinction.

    How were the #hcsm chapters categorized?

    • Audun Utengen, MBA

      Hi Colleen!

      Indeed, one of the major attractions of Twitter has been its force of removing boundaries of all kinds. A spot in the world where all users are created equal, individuals and organizations alike. This force extends to geographical and professional boundaries.

      All the “#hcsm”s were categorized as “professional/provider topics”. There is no true black and white (or should we say green/pink), but segmentation will always be very helpful to paint a picture of what is taking place. In the next couple of blogs in this series we will take a closer look at a couple of these patient centric communities. From looking at the dynamics of these communities, and then a closer look at the members, we will talk directly to this point. Stay tuned!

  11. Louise Aronson

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  34. Graham Steel

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  36. Dvorah Graeser

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  37. Dvorah Graeser

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  39. Ritesh Patel

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  51. Iain Scott

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  55. Blue Shield of CA

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  61. Regina Holliday

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  62. Shannon Ragland

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  67. Michael Josefowicz

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  68. Michael Josefowicz

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  69. P. F. Anderson

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  71. visualligence

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  72. Berci Meskó, MD, PhD

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  88. Pediculosis Assoc

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  89. Pediculosis Assoc

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  90. Steven Ferguson

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  91. Joseph Kim, MD

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  92. Paul Boidy

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  94. Ritesh Patel

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  108. Mark Harmel

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  117. SashaKane

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  118. Mike Thompson, MDPhD

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  119. Aiden Marketing, Inc

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  125. KRHDHealthLaw

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  126. Noble Vision Group

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  127. Kim Henshaw

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  128. Sam Bruinewoud

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  130. Thomas M. Lee

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  131. Dr. Dewey Jones IV

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  132. Markoneinfour

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  133. Trevor Lakey

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  134. Jo

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  135. Richard McCrea

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  138. Lisa Fields

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  139. Ann Bartlett

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    http://t.co/mc0K5XJ3
    #HCLDR

  140. Lisa Fields

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  141. Lisa Fields

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  142. Alex Riegler, EMPH

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  143. Françoise L'HEVEDER

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  144. Bernhard Danzer

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  145. Ali R. Jalali

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    Rise of Patient Communities=Twitter – Twitter Visualized
    http://t.co/mc0K5XJ3
    #HCLDR

  146. Lisa Fields

    Thanks @AnnBartlett 4 Tweet MT Almost 150 million health tweets in @symplur database Twitter Visualized http://t.co/mc0K5XJ3 …cc: @symplur

  147. Mike Thompson, MDPhD

    The Rise of Patient Communities on Twitter – Twitter Visualized http://t.co/fl4xcBP0 via @Symplur

  148. Anas Younes, M.D.

    Very interesting…RT @mtmdphd: The Rise of Patient Communities on Twitter – Twitter Visualized http://t.co/KVlvXkY4 via @Symplur

  149. Dr. Luis Villela

    The Rise of Patient Communities on Twitter – Twitter Visualized http://t.co/fl4xcBP0 via @Symplur

  150. Treato

    Awesome visuals of the rise of twitter patient communities http://t.co/204SDAed

  151. QuantifiedSelfTrends

    The rise of patient communities: visualized http://t.co/5PwM2B6M

  152. The Rise of Patient Communities on Twitter - Twitter Visualized | Internet Medicine | Scoop.it

    […] For over 2 years, Symplur has collected health conversations on Twitter. From a humble start, our infrastructure has grown and matured to scale with the tremendous growth of healthcare social media. We’re now approaching 150 million health tweets in our database which we analyze and categorize by thousands of variables. And similarly with other repositories of big data, our greatest challenge is to figure out how to present insights from such a vast dataset in a meaningful way. The answer is often found in visualizations…  […]

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    […] Twitter Visualized The Rise of Patient Communities on Twitter The Dynamics of a Twitter Patient Community – Network Centrality Analysis The Fl  […]

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