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This is a 300dpi map of the top 50 PR twitterers (as per Stephen Waddington's analysis) and the interrelationships between them.
To generate this:
We first crawled all the accounts for "friends" (accounts that they follow) and "followers" (accounts that follow them). This is a profligate use of resources because we were always going to throw away a massive load of that data. But it's always more interesting to start with a large data set. You don't know what you're going to find.
Then I wrote a quick-and-dirty perl script to process the data looking only for those instances where one of the top 50 followed another.
Then we dropped everything into NetDraw (if you are at all interested in this stuff, you really should get hold of a copy and start reading around the subject.) We laid out the chart so that the people who have the most peer-group followers are in the centre of the chart - and to make it even more obvious, we sized their nodes according to the number of peer-group followers that they have.
So people on the peripheries (like me - mediaczar) are peripheral to the community, and those in the middle are central. Obvious, huh?
This chart already shows a massive difference between our analysis (as it progresses) and the raw data from Wadds's list. There are some really good reasons for this, which I'll go into on the blog.
For more on this, see this post on Twitter Social Network Analysis
Dr. Stuart Borrett presenting about the rise of network ecology in academic ecological publications. Network models are broadly useful for ecological research questions because the model is explicitly relational as are most ecology questions. This was a seminar at the Free University of Berlin. Picture taken by Oksana B.
Dollywagon's latest network analysis of hash tag usage on Twitter during the current General Election raises some interesting questions.
The official election polls in the mainstream media show a tight race with the Conservatives slightly ahead of Labour and The Liberal Democrats. However, on Twitter the picture appears to be significantly different.
Our data shows that political hash tag usage related to the Lib Dem party is approximately 50% higher than that for Labour and 75% higher than for the Conservatives.
The Lib Dems have clearly won a greater share of Twitter traffic than the other two main parties. But does this reflect something the official polls are not picking up or does it mean that Twitter (and other social media) have failed to make a decisive impact on this election campaign?
Dollywagon's guess is that Twitter's role has been limited to providing an outlet for partisan views and the sharing of General Election "Oh My God" moments.
Our latest General Election Hash Tag Cloud image supports this view - the picture reveals an interesting evolution from yesterday's situation. The orange Lib Dem cloud at the top of the image is as populous and dense as ever.
But at the bottom of the picture we see a red surge of Labour supporting tags - this seems less to do with a spontaneous popular outpouring rather than a concerted effort by party activists to raise morale amongst the core vote.
Unfortunately for Labour we also see a small mushrooming of tags related to Manish Sood, the maverick Labour candidate in Norfolk who denounced Gordon Brown. But looking on the bright side, at least the 'bigotgate' hash tags seem to have mostly boiled away.
Positive Conservative tags seem less in evidence - they are definitely there but tend to be swamped by larger numbers of negative-Tory tags. The purple #philippastroud tags are a case in point - they have been the fastest growing hash tags of the last 48 hours but their key themes have failed to leak into the mainstream media.
This suggests that as far as UK politics is concerned, Twitter may become an enclave for left-of-centre and liberal views that are antithetical to mainstream conservative mores. Our view is that the cultural pendulum has swung away from the concerns of the liberal left, which partly explains the lack of interest in a 'Tory Homophobes' story among the main TV channels.
However, if David Cameron does becomes our next Prime Minister, we predict that Twitter could become a significant platform for a future social and political back-lash against Tory rule.
Twitter doesn't seem to have rocked the boat too much during this General Election (TV certainly did), but when the cultural pendulum swings back towards the liberal left it could just set the agenda for the next election.
To download a free report follow this link: www.slideshare.net/Dollywagon/has-twitter-had-any-impact-...
Results of an IssueCrawler exploration of the Australian political blogosphere's coverage of the David Hicks case, in March 2007. See snurb.info/node/634 for details.
Results of an IssueCrawler exploration of the Australian political blogosphere's coverage of the David Hicks case, in March 2007. See snurb.info/node/634 for details.
Results of an IssueCrawler exploration of the Australian political blogosphere's coverage of the David Hicks case, in March 2007. See snurb.info/node/634 for details.
This map shows the relationships between Westminster (UK) Members of Parliament on Twitter on 9 February 2009. I used NetDraw's Analysis>Subgroups>Factions function to calculate party affiliation. The calculation differs by only one element (93.75% accurate.) The sample group is very small, but it seems like this may be a useful tool for future exploration!
Results of an IssueCrawler exploration of the Australian political blogosphere's coverage of the David Hicks case, in March 2007. See snurb.info/node/634 for details.
Results of an IssueCrawler exploration of the Australian political blogosphere's coverage of the David Hicks case, in March 2007. See snurb.info/node/634 for details.
From data shared by Matthijs, whom I met at the eSocial Science conference. This shows a bipartite network of people and projects funded by the UK eScience initiatives; the people are circles and the projects are squares, or maybe it's the other way around... The color and size of the nodes indicates degree; redder and bigger nodes have more connections than smaller and yellower nodes.
I haven't yet done any analysis, but it's an interesting little data set to play with when I have time to kill.
This is the accumulation of books from my two years at the University of Michigan's School of Information. The leftmost stack are books related to web analytics and information architecture (notably missing is the Polar Bear Book, which has already been packed up). The left middle stack are SI-worldview books and references from my network analysis course, SI 614. The right middle stack are usability and design books, and the rightmost stack are reading for complex systems courses. I've read nearly everything in entirety in both end stacks, and perhaps about half of the stuff in the middle two stacks.
Results of an IssueCrawler exploration of the Australian political blogosphere's coverage of the David Hicks case, in March 2007. See snurb.info/node/634 for details.
This map shows the relationships between Westminster (UK) Members of Parliament on Twitter on 9 February 2009. Actual party affiliation is shown by colour: where red => Labour, blue => Conservative, and yellow => Liberal Democrat.
Network analysis of 3600 18th century novels, showing the relative "influences" as defined by Jocker's text-analysis.
Al final han actualizado en parte las estadísticas de pases, pero solo hasta la página 13, así que la red es por un lado el agregado de varios partidos, y por otro lado incompleta. Algo se puede ver, de todas formas.
Network diagram displaying the relative influence 3600 18th century novels had on each other, as defined by Jockers topic modelling.
Image BIBLIO
Jockers, M., Computing and Visualizing the 19th-Century Literary Genome, Presented at DH2012 Conference, www.dh2012.uni-hamburg.de/conference/programme/abstracts/...
This is a network analysis graph about how countries voted each other in Eurovision 2007. For getting clearer graph, only the best voting points (8, 10, 12) is shown. The graph shows quite clearly that new EU countries in the Eastern Europe voted quite actively each other for some reasons but the older EU countries voted mostly the best performances.
On 9/29/2016 Miriam Posner presented a workshop on network analysis for film and media as part of the history and theory of new media lecture series at UC Berkeley. Supported by the Berkeley Center for New Media and Digital Humanities at UC Berkeley.
Fig. 1 Preliminary network of Eastern Sigillata C (150-125BC). Edges are drawn between production centres (Pergamon and çandarli) to places of deposition, each sherd follows only one path and is restricted to sites in which this ware was found.
Image Biblio
Gephi Website, web, accessed March 18 2014, gephi.org/features/
Copyright (C) 2008 – 2011 Gephi Consortium
Real-time network traffic monitoring and analysis are two core works to most enterprise network administrators. However, most administrators soon give up on network monitoring.
A fictitious two-mode network representing sites connected to pottery forms which are present on the site. The value indicates the number of sherds of a form that have been found.
A fictitious one-mode network representing pottery forms connected to other pottery forms which have been found on the same site (co-presence). The value indicates the number of sites on which both forms are co-present.
A fictitious one-mode network representing sites connected to sites which have evidence of the same pottery forms (co-presence). The value indicates the number of pottery forms that are co-present.
One-mode network of the period 150-125 BC, representing pottery forms connected to other pottery forms which have been found on the same site (co-presence). The value indicates the number of sites on which both forms are co-present.