Over the last few weeks, I’ve been tinkering with various ways of using the Twitter API to discover Twitter lists relating to a particular topic area, whether discovered through a particular hashtag, search term, a list that already exists on a topic, or one or more people who may be associated with a particular topic area.
On my to do list is a map of the “open” community on Twitter – and the relationships between them – that will try to identify notable folk in different areas of openness (open government, open data, open licensing, open source software) and the communities around them, then aggregate all this open afficionados, plot the network connections between them, and remap the result (to see whether the distinct communities we started with fall out, as well as to discover who acts as the bridges between them, or alternatively discover whether new emergent groupings appear to crystallise out based on network connectivity).
As a step on the road to that, I had a quick peek around found who were tweeting using the #oscon hashtag over the weekend. Through analysing people who were tweeting regularly around the topic, I identified several lists in the area: @realist/opensource, @twongkee/opensource, @lemasney/opensource ,@suncao/open-linked-free, @jasebo/open-source
Pulling down the members of these lists, and then looking for connections between them, I came up with this map of the open source community on Twitter:
Using a different technique not based on lists, I generated a map of the open data community based on the interconnections between people followed by @openlylocal:
and the open education community based on the people that follow @opencontent:
(So that’s a different way of identifying the members of each community, right? One based on lists that mention users of a particular hashtag, one based on folk a particular individual follows, and one based on the folk that follow a particular individual.)
I’ve also toyed with looking at communities defined by members of lists that mention a particular individual, or people followed by a particular individual, as well as ones based on members of lists that contain folk listed on one or more trusted, curated lists in a particular topic area (got that?!;-).
Whilst the graphs based on mapping friends or followers of an individual give a good overview of that individual’s sphere of interest or influence, I think the community graphs derived from finding connections between people mentioned on “lists in the area” is a bit more robust in terms of mapping out communities in general, though I guess I’d need to do “proper” research to demonstrate that?
As mentioned at the start, the next thing on my list is a map across the aggregated “open” communities on Twitter. Of course, being digerati, many of these people will have decamped to GooglePlus. So maybe I shouldn’t bother, but instead wait for Google+ to mature a bit, an API to become available, blah, blah, blah…