Cetis12 sna


Published on

Published in: Technology, Education
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Data – stuff… egarchive of twitter dataInformation – contextualised data, data fields interpreted as something, and data represented in a meaningful way; eg network of connections between tweet sender/recipient or retweeter/retweeted usersIntelligence – read the information and make sense of it; intelligence gives you some idea of what’s going on… eg X has high betweenness centrality and connects two subgroupsInsight – generate a deeper model about what’s going on; the fact that X has high betweenness centrality helps you understand/realise how X manages to come up with a particular crazy mix of ideas.
  • Through the provision of an API on top of the aggregated local council data, OpenlyLocal can also be treated as a database in its own right. In the example shown here, committee membership is displayed via a treemap showing party affiliations of committee members. (Hovering over a particular grouping displays a list of names of council members on that committee from that party political grouping.) Whilst it would be a major task to take data from every council website in a variety of formats in order to generate similar views for other councils, the work done by OpenlyLocal in aggregating this data and then republishing it via a single API in a single format means that the treemap view can be applied to each council whose data is stored in OpenlyLocal.In passing, it is also worth mentioning how the use of visualisations can be helpful in cleaning data or identifying possible errors in it. In the above example, we see that party affiliations for councillors on the Isle of Wight Council are declared as both Liberal Democrat and and Liberal Democrat Group.
  • ist 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:looks up the lists those candidate members are on to provide a set of “candidate lists”;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);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;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;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;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:one or more named individuals;the friends of a named individual;the recent users of a particular hashtag;the recent users of a particular searched for term;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.
  • Emergent Social Positioning: origins: 1.5 degree egonet (how followers follow each other, how hashtaggers follow each other)- projection maps from followers to folk they commonly follow;-- projection maps from hashtaggers to folk they commonly follow- projection maps from friends to folk who commonly follow them
  • Emergent Social Positioning: origins: 1.5 degree egonet (how followers follow each other, how hashtaggers follow each other)- projection maps from followers to folk they commonly follow;-- projection maps from hashtaggers to folk they commonly follow- projection maps from friends to folk who commonly follow them
  • Google Motion Chart, LinkedIn InMap
  • Cetis12 sna

    1. 1. It may (or may not) beinteresting, but is it useful? Tony Hirst, Dept of Communication and Systems, The Open University
    2. 2. d3i data information intelligence i nsight
    3. 3. In search of structure…
    4. 4. Hierarchical data and treemaps - medalsPivot tables
    5. 5. Macroscopes
    6. 6. digraph test { "[SPARQL]"->RDF; "[SPARQL]"->XML;CSV [shape=box] "[SPARQL]"->CSV;KML [shape=box] "[SPARQL]"->JSON;JSON [shape=box] JSON-> "<JQueryCharts_etc>";XML [shape=box] CSV->"{GoogleRefine}"RDF [shape=box] CSV->ScraperWikiHTML [shape=box] JSON->ScraperWikiGoogleSpreadsheet [shape=Msquare] "[YQL]"->ScraperWikiRDFTripleStore [shape=Msquare] ScraperWiki->CSV"[SPARQL]" [shape=diamond] HTML->ScraperWiki"[YQL]" [shape=diamond] HTML->"[YQL]""[GoogleVizDataAPI]" [shape=diamond] "[SPARQL]"->"[YQL]""<GoogleGadgets>" [shape=doubleoctagon] "{GoogleRefine}"->CSV [style=dashed]"<GoogleVizDataCharts>" [shape=doubleoctagon] CSV->"<Gephi>" [style=dashed]"<GoogleMaps>" [shape=doubleoctagon] "<Gephi>"->CSV [style=dashed]"<GoogleEarth>" [shape=doubleoctagon] RDF->"[YQL]”"<JQueryCharts_etc>" [shape=doubleoctagon] }
    7. 7. Network structure Node and edges All nodes the same sort of thing Edges may be directed or undirected Edges may be weighted Bipartite graph – two sorts of nodes Can collapse a bipartite graph to get a new view over the data
    8. 8. Categories become defined byrelations between entities, rather than the top down action of a cataloguer
    9. 9. FollowerCommunities
    10. 10. Couple of network graphs to make the point…
    11. 11. HashtagCommunities
    12. 12. How do hashtag users follow each other?
    13. 13. (“1.5 degreeegonet” around a hashtag)
    14. 14. Folk on lists @jisccetis is on
    15. 15. The bipartite graph version…
    16. 16. How folk on lists follow each other
    17. 17. ESP
    18. 18. Emergent SocialPositioning
    19. 19. Who do my followers follow?
    20. 20. Who follows my friends?
    21. 21. Layout
    22. 22. Sizingandcolouringno des
    23. 23. Commonalitiesand differences
    24. 24. Aquote from this months Racecar Engineering,in a comment piece by Paul Weighell:“ Whitworths core concept was accuracy inmeasurement, which is what we would termtoday ‘enabling’technology. ... It is, I believe,still a general rule that technology advancesby first improving standards of measurementand accuracy.”
    25. 25. Static and dynamic analysis ofnetworks Structural vs traffic analyses
    26. 26. Social network data that’s there forthe taking… Twitter Google+ Facebook Delicious Email Bitly(?)
    27. 27. Ethics…
    28. 28. blog.ouseful.info@psychemedia
    29. 29. reports/scmvESP/scmvESP_2012-02- 18-14-31-53 JISC follower netwrok – interactive gephi playtime ? Second session?