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Visualizing a medical Twitter hashtag: MDChat

I’ve recently got in touch with an amazing group, the Thesys Group. They invited me to their HQ to show me what kind of projects they are working on and we started a bit of brainstorming about what we could come up with together.

In our first project, the Thesys Group analyzed the network of discussions focusing on one of the most popular medical Twitter hashtags, MD_chat. In the figure below, a dot represents a Twitter user, lines connecting the dots represent their relationship. The bigger the dot is, the more tweets the Twitter user had. The thicker the line is, the more tweets the two users had with each other. Based on this, here is the network graph (click on the image below to access the interactive graph):

Dots in the middle account for active users, while dots in the periphery did not participate that often in these discussions. Graph includes only tweets including user names, therefore representing discussions.  Here is a zoomed version of this graph just to show you how the dots are connected to each other on a smaller scale with @doctor_v and @jodyms in the focus.

A few numbers and facts:

  • Tweets are dated between October, 2010 and October, 2011 (4815 messages).
  • Data tables were obtained from a public Scridb database containing all the MD_chat discussions and can be downloaded in doc or PDF formats.
  • 282 users are represented in the graph with 1972 connecting them to each other.
  • Graph was visualized with the Gephi open-source platform.

The top 10 most active Twitter user using the MD_chat hashtag in discussions (largest dots in the graph):

Rank MD chat user name Number of addressed tweets
1 richmonddoc 559
2 ellenrichter 204
3 gailzahtz 190
4 peds_id_doc 181
5 mdstudent31 178
6 apjonas 159
7 ability4life 155
8 westr 145
9 chukwumaonyeije 140
10 md_chat 139

The aim of this short study was to point out the importance of medicine related hashtags and the growing popularity of these. The dynamic growth of MD_chat is a good example for the changes that we can see now in the everyday communication among peers. Therapeutic experience, news and opinions spread without geographical or linguistic limitations.

Please let us know what you think of this analysis and feel free to contact me or the Thesys Group for more details.

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7 Comments Post a comment
  1. very cool. looks like spirographs i would make as a kid! would love to see one for #meded

    June 3, 2012
  2. Really awesome analysis, Dr. Meskó! Thanks for sharing these findings!

    June 25, 2012
  3. Our pleasure!

    June 26, 2012
  4. Beautiful graphics! Love the analytics.

    BTW. The original tweet database source is @symplur
    (of course I had to point that out :))

    June 27, 2012
  5. Editor #

    Reblogged this on Health Care Social Media Monitor.

    September 28, 2012

Trackbacks & Pingbacks

  1. Top Medical Social Media Stories of 2012: Month by Month « ScienceRoll

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