Tag Archives: Mobile health

Ingrid Mason and Luc Small delivering the closing address of THATcamp Sydney 2013

#THATcamp Sydney, October 2013 Review

We recently presented some of our social network analysis research of the informal policy actors within the mobile health regulatory space at the THATcamp Sydney unconference. Unfortunately I missed the first day and Fiona could only attend the first session of that day, but we managed to see a full (half) day on the second.

Amongst other great projects I observed, one that certainly stood out for me was the Australian’s Women’s Register. In conjunction with the University of Melbourne, they are doing some outstanding work on collecting and analysing data about Australian women – highly recommend checking this out.

Fiona and I then conducted our session which we took as an opportunity to talk about our work so far and use expertise in the room to interrogate and develop our methodology. It was an amazing experience and you can hear the audio from our talks here.

Some of the points that emerged from the discussion include:

  • Who are the official organisations that are interacting in these conversations?
  • There is existing research to suggest that participation is driving policy
  • Debra Lupton at the University of Sydney to explore data and politics
  • Nick  Thurburger University of Melbourne, experience and developing methodology in converting Excel sheets to data clean http://languages-linguistics.unimelb.edu.au/thieberger/
  • Thresholds of data – what to use, who are they, where are they, rural voices etc – we need to think about our own thresholds which shape the data analysis
  • Demographic profiling in the process – this is critical to understand the ‘why’ of the interactions between the actors
  • Content coding around the data collection, who do we want to hear from?
  • Jake Wallace Charles Stuart university, experience in political party process
  • Policy analysis portal – I am thinking we need to develop a tool similar to this to embed in our site – that is users can bring their data to it and run their own analysis
  • Positive and negative sentiment in tweets – UWS are working in this space
  • Government institutions are legally required to collect social media conversations – interesting!
  • Internal organisational cultural behaviours influencing what is said  and what is not said via freedom of information act
  • If so, could we access Yammer data?
  • Digital engagement as opposed to social media
  • Atlas of living Australia dashboard.ala.org.au - great site to play around with in representing data
  • Internet archive to send or harvest bit.ly links – credibility of user data in government policy
  • Suirveillance in terms of peaks of use, who is using soc med and when
  • Gnip.co twitter api – this is the ‘fat tube’ of Twitter data and Fiona is working on a collaborative approach with UTS
  • Steve Cassidy at Uni of Macquarie – workflow people http://web.science.mq.edu.au/~cassidy/

And if that’s not enough, the wonderful Yvonne Perkins kept a Google doc of the session which can be viewed online.

Great session and i think it is a fantastic primer for our paper which we will present at the aaDH (Australasian Association of the Digital Humanities) conference in 2014 – thanks for the input THATcamp!

Week 1 of mapping the mobile health policy actors

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:

Comp20130614

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)

Comp20130613Filtered

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.

The combined conversation around healthapps, mhealth, apps and FDA

Mapping the mobile health policy actors: Who is talking to whom on Twitter, and to what effect?

This is a methodological post on some social network analysis work we are developing for Moving Media. The premise for the SNA research is reasonably simple:

Task: Perform social network analysis around the Twitter conversations about the FDA’s proposed health apps guidelines, posted July 19th 2011:

Public brief: http://www.fda.gov/forconsumers/consumerupdates/ucm263332.htm

Document: http://www.regulations.gov/#!documentDetail;D=FDA-2011-D-0530-0001

Comments and Submissions: http://www.regulations.gov/#!docketBrowser;rpp=25;po=0;dct=PS;D=FDA-2011-D-0530;refD=FDA-2011-D-0530-0001

Aim: To map the dispersed network of actors discussing the FDA policy consultation process in social media channels, visualising their relative influence and communicative relationships.

After some initial Twitter research, we found the #FDAApps hashtag to be the conversation we wanted to analyse. The only drawback is that this conversation seems to be unreachable – the Twitter API didn’t return anything although the conversation is there. Any suggestions on this would be appreciated. Following on from this I did a search across four conversations: #mhealth, #healthapps, #FDA, #apps. It is an experiment in both the methodology and the content.

Here’s the breakdown on the process (and it gets a bit nerdy from here):

1. I tracked four Twitter conversations (#mhealth, #healthapps, #apps & #FDA) and processed the data through the Twitter API, Open Refine and then into Gephi. I imported the .csv file into Open Refine to extract the @replies and the #hashtag conversations – a process of deleting much of the data and producing a .csv file Gephi likes. I then imported the data into Gephi, ran a Force Atlas and Frutcherman Reingold layout and ranked the labels by degree. I then played with the statistics slightly by running a Network Diameter across the network (Average Path length: 1.0508474576271187, Number of shortest paths: 236), which enabled my to colour the labels via their betweenness centrality on a scale of 0 – 6, Eccentricity 0-2 and closeness centrality 0 – 1.5. I then ran a modularity stat across it (Modularity: 0.790, Modularity with resolution: 0.790, Number of Communities: 18). 18 communities!

 

2. I did this for each set of data, that is #mhealth, #mobilehealth, #healthapps, #apps and #FDA. Each process provided a visualisation that demonstrates the key conversation hashtags and the most significant people in those conversations. Here’s the preliminary analysis:

#healthapps conversation

#healthapps conversation

FDA20130528

#FDA conversation

#apps conversation

#apps conversation

#mhealth conversation

#mhealth conversation

3. I then combined the cleaned data of the four conversations together to create a ‘super set’ to understand the broader ecology of the policy discussion around mhealth and health apps.

The combined conversation around healthapps, mhealth, apps and FDA

The combined conversation around healthapps, mhealth, apps and FDA

Preliminary analysis: What we know (and this is my first critical analysis of this process – it could change as I become more aware of what is going on here):

  • The conversation between the FDA and healthapps is stronger than the other two topics due to its location in the network
  • @Vanessa_Cacere is the most prominent twitter user in #apps (she often retweets our tweets too!)
  • @referralIMD is prominent in #mhealth
  • @MaverickNY is prominent in #healthapps
  • The bluer the colour of the actor, the closer they are to the topic – ‘closeness centrality’
  • @Paul_Sonnier [https://twitter.com/Paul_Sonnier] is extremely significant in the overall conversation – ‘betweenness centrality’
  • There are some other probably other significant terms here like #digitalhealth, #breakout, #telehealth, #telemedicine
  • It sucks some CPU processing power
  • The healthapps viz did not work so well, and I’m not sure why.

The limitations as of now:

  • This isn’t the #FDAApps conversation from July 2011 on, this is the mhealth conversation of the 28 May 2013
  • I’m not entirely sure it’s possible to construct an archive from events past – I need to look into this further
  • I think I can code a program that pings the Twitter API automatically every 20 seconds and then automatically adds it to the dataset. If I can build this, we can start tracking data from now on issues/conversations we think are important. I am manually doing this now, but it is really laborious.
  • There is conversations around #apps in general here too. A proper analysis will likely need to clean the raw data further to eliminate any inaccuracies of the representation

Any input on this process would be greatly appreciated and if you have any insights on the findings, please comment below.