A Couple of Notes on “List Intelligence”

Just so I don’t forget the development timeline such as it is, here are a few quick notes-to-self as much as anything about my “List Intelligence” tinkering to date:

• List Intelligence uses (currently) Twitter lists to associate individuals with a particular topic area (the focus of the list; note that this may be ill-specified, e.g. “people I have met”, or topic focussed “OU employees”, etc)
• List Intelligence is presented with a set of “candidate members” and then:
  1. looks up the lists those candidate members are on to provide a set of “candidate lists”;
  2. identifies the membership of those candidate lists (“candidate list members”) (this set may be subject to ranking or filtering, for example based on the number of list subscribers, or the number of original candidate members who are members of the current list);
  3. for the superset of members across lists (i.e. the set of candidate list members), rank each individual compared to the number of lists they are on (this may be optionally weighted by the number of subscribers to each list they are on); these individuals are potentially “key” players in the subject area defined by the lists that the original candidate members are members of;
  4. identify which of the candidate lists contains most candidate members, and rank accordingly (possibly also according to subscriber numbers); the top ranked lists are lists trivially associated with the set of original candidate members;
  5. provide output files that allow the graphing of individuals who are co-members of the same sets, and use the corresponding network as the basis for network analysis;
  6. optionally generate graphs based on friendship connections between candidate list members, and use the resulting graph as the basis for network analysis. (Any clusters/communities detected based on friendship may then be compared with the co-membership graphs to see the extent to which list memberships reflect or correlate to community structures);
• the original set of candidate members may be defined in a variety of ways. For example:
  1. one or more named individuals;
  2. the friends of a named individual;
  3. the recent users of a particular hashtag;
  4. the recent users of a particular searched for term;
  5. the members of a “seed” list.
• List Intelligence attempts to identify “list clusters” in the candidate lists set by detecting significant overlaps in membership between different candidate lists.
• Candidate lists may be used to identify potential “focus of interest” areas associated with the original set of candidate members.

I’ll try to post some pseudo-code, flow charts and formal algorithms to describe the above… but it may take a week or two…