My inner geek is tingling this morning. After a pretty big night on the data, I woke with a visualisation hangover. But the good news is we now know mobile health data stuff.
I have established some significant methodological approaches this week given that Twitter shut down their Search API on Friday last week just after we had confirmed our approach. Essentially, I had to switch to their new Streaming API which then enabled me to construct an archive on #mHealth, #mobilehealth and #healthapps. In the seven days, we gleaned 7229 #mhealth tweets, 453 #mobilehealth tweets and 277 #healthapps tweets (total 7963 tweets). This is automatically scrapping the Twitter API now and we continue to collect the data (which is great because I just found out Gephi has a timeline function so we can track this conversation and then animate it).
So this is what the larger dataset looks like:
I then started drilling into the statistical make up of this conversation. It emerged there are 1463 communities conversing around these topics. The next graphic is really useful because we can see who the lead users are and the networks they influence (this is the cool bit)
What we can see here is the lead influencers are: @PhilippeLoizon, @Paul_Sonnier, @EricTopol and @Saif_Abed by quite a significant amount. If we drill a little further we can see the top twenty influencers of the 1463 communities are: @StefanieMastny, @RarusRarus, @JessWa21, @mobilehealth, @HealthcarePays, @NewsForToday1, @sound_wordz, @Ustabilize, @sandraproulx, @ideagreenhousnh, @pttalk, @Techlog, @bkalis, @Brian_Eastwood, @laurenstill, @danmunro, @RSpolter. #Kenratt, @Perficient_HC, @HealthStandards.
So the next step to follow is to find out who these people are within the health apps ecology as they are highly influential – well in the Twitter sphere at least.