Care happens in networks. People take care of each other. They seek advice, medical help and moral support from each other. They exchange knowledge and share resources. They meet, interact, and work together. And, of course, no human can live well if he or she disconnects from the fabric of society at large (in recent times, care also happened in big bureaucracies, but that approach has issues. Here we look for something better).
We think that this ceaseless exchange is collective intelligence at work. Network analysis is a useful tool to understand this process, and perhaps find ways to improve upon it. Thinking in networks is a great way to generate fresh, relevant questions. How do you know your network is going in the right direction? What is a “direction” in this context? Is everyone following the same path? Do people group into sub-communities? What are the focus of these (sub) communities?
We come together to find out how networked humans can better take care of each other.
To do this, we study result-oriented conversations. Conversations are networks: people are its nodes, and the exchanges are its links. It you don’t believe us, click here to explore the Edgeryders conversation network (allow a few seconds for the data to download). But conversations are networks also in another sense: each exchange contains some concepts. Example of concepts useful in care are: well-being, syringe, diabethes, fitness, prosthetics, etc. We can represent concept in a conversation as a network. Concepts themselves are its nodes; two concepts are linked if they are in the same exchange.
Person-to-person conversation networks tell us who is talking to whom. Are there individuals who act as “hubs”? Why? Can we use hubs to improve the process, for example asking them to spread important knowledge?
Concept-to-concept conversation networks tell us how the different concepts connect to each other. Are there surprises? Do apparently unrelated concepts tend to come up in the same exchanges? Anomalies might mean something interesting is going on. In fact, spotting anomalies is how John Snow invented epidemiology in 1854.
The fascinating part is this: by looking at the network, we can extract information that no individual in the network has. The whole is greater than the sum of the parts. Collective intelligence!
We look at conversation data taken from Edgeryders and build them into a network. We use open source software for network analysis. We then visualize and interrogate the network to see what we can learn. Our final aim is to prototype methodologies for extracting collective intelligent outcomes from conversations.
One great output from the workshop would be to unleash our imagination, and specify design & requirements:
- What views works best? It'll be useful to build it as a mockup if we do not already have it -- use color pens, paper, clips, cardboard and build it into a mock-up!
- For what tasks? Do we need to move things around? Pile them up to trigger comparison of things on-the-fly? Lasso an item to trigger some computation? -- use post-it notes, cut and paste pieces of paper, draw arrows to turn tasks into real actions (on a screen!).
- Using what ingredient (data)? What should we feed the system with to accomplish these analytical tasks? -- write them down, cut & paste, associate them with specific tasks, embed them into views.
The workshop is a unique opportunity to have a design participatory workshop – we want it to be a source of inspiration to design and build the next generation EdgeSense dashboard!
Who should come
Masters of Networks is open to all, and especially friendly to beginners. Patients, network scientists, doctors, hackers and so on all have something to contribute. But in the end we are all experts in this domain. We all give and receive care in the course of our lives, and all humans are expert conversationalists. There’s an extra bonus for beginners: networks are easy to visualize. And when you visualize them, as we will, they are often beautiful and intuitive.
Trust us. We have done this before (check out the video above).
We have a dataset drawn from a large conversation that took place on the Edgeryders platform in 2014. It consists of 161 posts and 910 comments, authored by 128 different people. All posts and comments have been annotated by a professional ethnographer. This leaves us with an ontology of relevant concepts: we can use it to build the network.
That conversation was not about care. We will need to be clever, and use different data to figure out a methodology to apply to a future conversation about care.
Agenda and challenges
The agenda is simple:
- We will spend the first hour and a half explaining how the data were formed, harvested and converted into a network. We will explore the network together using a software called Detangler, brainchild of the wonderful @brenoust. Detangler is highly intuitive: we can use it to manipulate network without knowing any network math at all.
- Then, we'll hack. We can explore the data in many directions. Depending on how many we are, we can split into groups that look at different things. We see at least three possibilities:
Visualization challenge. Create informative and beautiful visualizations starting from our data. Skills needed: design, dataviz, netviz. Coordinator: @melancon (you can call me Guy)
- Its not only about creativity and beauty, it's about interactivity -- a map seen as a malleable object so you can squirk information out of it.
- It's also about being able to specify graphical design from the tasks you'd need to conduct on the data and its representation on the screen.
- How is a node-link view useful? How would you intuitively like to manipulate, filter or change it at will when exploring it?
- Would you feel you need to synchronize the view with a bar chart on some statistics? A scatterplot to figure out if things correlate?
Interpretation challenge. How many conclusions and hypotheses can we “squeeze” from the data? Skills needed: social research, ethnography, network science. Coordinator: @Noemi?
- Interpretation is at the core of the process. You play with data, you map it, and iteratively build hypothesis. In the end, you dream you would have provable claims.
Quality challenge. Can we think of simple criteria to filter the data for the highest-quality content only (eg: only posts with a minimum number of comments, or of minimum length)? Does the filtering change the results? Coordination: @Alberto
And more. But we insist that every group has a coordinator, who takes responsibility for driving it, sharing the relevant material (examples: software libraries, notes for participants, pseudo code…). If we only have two coordinators, we’ll only have two groups. If you think you can lead a group, get in touch with us!
- 9h30 - 11h - @Alberto and @melancon (@Hazem @Noemi) give a start by drawing the overall picture, following the famous adagio "A picture is worth a thousand words".
- 11h - 12h30 Teams give it a first shot
- The viz team will play a game building their ideal visual dashboard using pen and paper, cardboard -- explaining why these features may turn to be essential when exploring or analyzing network data.
- The interpretation team output is critical: the directions they will provide has decisive impact on how data will be used, massaged and turned into visual representations.
- The qualitative team plays a similar role, feeding the intepretation team with high quality content -- their recommendations will make even greater sense if we can link them with paths of interpretation.
- 12h30 - 14h Feed your brain with proteins and glucids.
- 14h - 16h Teams go back to work and build a proof-of-concept of the ideas /hypothesis they came up with in the morning session.
- Cross-fertilization of ideas with the other teams is encouraged. People may wish to change teams to widen their experience and knowledge.
- Teams prepare a short summary of their findings/conclusions that will be presented during the wrap-up session.
- 16h - 16h30 Wrap-up. Team presentation, plenary discussion.
- A network scientist: Guy Melançon, University of Bordeaux.
- A medical doctor: Marco Manca, CERN and ScImpulse Foundation
- A policy maker: Lucia Scopelliti, City of Milan
- An economist: Alberto Cottica, Edgeryders.
When it is, where it is and how to participate
Masters of Networks 4: Networks of Care is part of LOTE5. It takes place on Saturday, 27th February 2016 at Brussels Art Factory, SmartBE. Sign up by clicking the "attend button. Leave a comment below to let us know what your skills are, we’ll put them to good use! We particularly need people to help us with the documentation of what is done.
How to prepare
Have a look at Detangler, and play with the map just to get a feeling of what can be done. If you have questions, write them as comments to this post.
What happens next
A project called OpenCare will convene a large-scale conversation about care. The work in OpenCare will make good use of the insights generated during Networks of Care.
Date: 2016-02-27 08:30:00 - 2016-02-27 08:30:00, Europe/Brussels Time.