The Dynamics of a Twitter Patient Community – Network Centrality Analysis

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

Audun Utengen - @audvin

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

77 Responses to “The Dynamics of a Twitter Patient Community – Network Centrality Analysis”

  1. Thomas M. Lee

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  11. GHLF

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  16. Mike Thompson, MDPhD

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  17. Mike Thompson, MDPhD

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  18. Mike Thompson, MDPhD

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  19. Mike Thompson, MDPhD

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  20. Mike Thompson, MDPhD

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  28. Mike Thompson, MDPhD

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  29. Mike Thompson, MDPhD

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  30. Mike Thompson, MDPhD

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  35. Mike Thompson, MDPhD

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  36. Mike Thompson, MDPhD

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  37. Mike Thompson, MDPhD

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  50. Audun Utengen

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