The opencare conversation is getting very rich. We have big numbers (over 2,100 contributions from over 170 individual) and stellar quality. I highly recommend plunging into the fantastic stories that are coming through every day. If you don’t know where to start, you could follow our daily recommendations on Twitter/email, or just ask @Noemi .
We also need to start looking at the conversation as a whole, as a collective intelligence phenomenon. Edgeryders has had APIs for a while, and yesterday I played a little bit with them to generate a snapshot of the opencare conversation network. It’s quite beautiful:
Node size maps to length of incoming comments (receiving more, longer comments makes your node bigger). Edge color maps to the word count in that communication (a bright red edge from Bob to Alice means Bob has written more, longer comments addressed to Alice).
You can see the structure. There are three types of nodes. In the center, you see dense conversation of protagonists: @Rune , @Alex_Levene , @ybe , @Yannick , @WinniePoncelet , @maymay , @woodbinehealth , @Pauline , @Rozina , @Aravella_Salonikidou and many others. In the periphery, we have people who have had limited interaction with the rest of the network, often through only one person. We even have a few isolated nodes, though I am sure that will change quickly. These are participants that have not shared a story of their own, but have left one or a few comments somewhere. Finally, you have “hubs”, participants that serve the purpose of connecting the central nodes with the peripheral ones. @Noemi , @Nadia and myself are the highest-connected ones.
This is confirmed by subcommunity detection. Below you see the same graph, but color-coded for clusters. Hubs are strong enough that each generates her own subcommunity, shaped like a star with the hub at the center; but, to the southeast of the graph, you also see two subcommunities with a more complex structure than a star (they grey-blue one and the purple one). These are forming now, as people in opencare talk more and more to each other rather than to us.
None of this is semantic yet. But we are working on it: soon we’ll be able to show what people are actually talking about in those interaction.