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):
||MD chat user name
||Number of addressed tweets
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.
Clinical Current came up with a leaderboard of the most active users using the Twitter hashtag #hcsm (healthcare social media). I’m glad to be on the top, but it only means I’m active in this area. The scores are a mixture of Klout scores and activity.
The reason why I’m showing this to you now is that next week, I’m going to publish here a very detailed and thorough analysis of a particular medical Twitter hashtag and also visualize the results. Stay tuned!
Social media is changing how medicine is practiced and healthcare is delivered. Patients, doctors, communication or even time management, everything is changing, except one thing: medical education. We need a revolution!
When a UK physician wanted to visit Hungary every week just to attend my university course focusing on social media and medicine, I decided it’s time to make this course global.
Today, The Social MEDia Course goes live with 16 flash Prezis, exciting tests, badges and achievements. Enjoy and have fun while learning! Medical students, physicians and even patients, everyone is welcome to take the course which is, of course, for free.
Here is a video about the course (and also a Prezi).
Do you remember when more and more medical professionals started blogging 5-6 years ago and the Modern Language Association published a guide about citing a blog? Now here is the new format for citing a tweet in an academic paper.
Begin the entry in the works-cited list with the author’s real name and, in parentheses, user name, if both are known and they differ. If only the user name is known, give it alone.
Next provide the entire text of the tweet in quotation marks, without changing the capitalization. Conclude the entry with the date and time of the message and the medium of publication (Tweet). For example:
Athar, Sohaib (ReallyVirtual). “Helicopter hovering above Abbottabad at 1AM (is a rare event).” 1 May 2011, 3:58 p.m. Tweet.
The date and time of a message on Twitter reflect the reader’s time zone. Readers in different time zones see different times and, possibly, dates on the same tweet. The date and time that were in effect for the writer of the tweet when it was transmitted are normally not known. Thus, the date and time displayed on Twitter are only approximate guides to the timing of a tweet.
Yesterday, I wrote about a self-edited directory of European healthcare professionals on Twitter which was launched by Andrew Spong after I tweeted that I’m the only European doctor in the top 25 of the global list of doctors on Twitter. Now here is the interactive map version. This project is getting more and more attention and hopefully this movement will result in a very useful list of European medical professionals being active on Twitter.
Yesterday, I tweeted that I’m the only European doctor in the top 25 of the global list of doctors on Twitter, but I know there are many European doctors using Twitter quite massively. Responding to my tweet, Andrew Spong launched a self-edited database or directory of European doctors (actually all healthcare professionals) on Twitter. Feel free to add yourself.