Yesterday we presented our paper Community Core Detection in Twitter - a “Bottom Up” Heuristic. at Sunbelt Conference in Riva del Garda. Got a lot of valuable feedback. Thanks!

Time to publish our paper on:
Community Core Detection in Twitter - a “Bottom Up” Heuristic

In this paper we present a very lightweight heuristic for detecting cores of expert communities within twitter. The heuristic combines simple text search methods with social network analysis. One big advantage of this heuristic is that it needs not to be run over the whole network. As a “bottom up” approach it explores the network around seed accounts and detects communities with simple measures.

in our recently published tool twitter list explorer (tlx) beta we don’t used that heuristic as we simply read in twitter lists, which may be seen as manually extracted community cores (which turns out not to be true in many cases, because there are several reasons for collecting twitter accounts in a list).
Anyhow it is the plan to integrate such a heuristic in one of the next versions.

The paper will be presented at the next sunbelt conference 2010 in Italy.

We proudly annonce twitter list explorer (tlx) beta

The “twitter network explorer” lets you visualize and explore Twitter lists:

• Enter any Twitter list URL, e.g. @twitter/team
• Hit “Explore” button
• Depending on size and popularity of the list loading may take up to one minute
• In some cases the network doesn’t load properly; therefore try several times, inlcuding “Shift+Reload”. (Remember: it’s “beta” – and Java
• After loading, please read the information in the “Network” tab.

Extract from our research, done with pajek and self collected twitter data:

twitter team list from here:

http://twitter.com/twitter/team

extracted list manually:[twitter_team_list]

the lists consists out of 121 accounts

i exctract the 1 all neigbourhood and get a network consisting out of 5391nodes.

here are the top 40s - all degrees

Rank  Vertex  Cluster     Id
——————————–
1    1768     1011     ev
2    1006      861     biz
3    2016      393     mashable
4    5159      387     twitter
5    2463      291     delbius
6     830      289     al3x
7    2042      245     caroline
8     741      216     scobleizer
9    1278      207     nk
10    4740      199     charles
11     940      196     jess
12    1342      180     laura
13      50      163     jack
14    4419      163     techcrunch
15    4121      157     twitter_tips
16     462      156     twitterapi
17     131      154     drew
18    1742      142     garyvee
19     905      136     buzzedition
20    4009      132     iconic88
21    1368      127     foursquare
22    1378      122     billzucker
23     840      121     heykim
24    4864      116     raybeckerman
25    2187      113     mayhemstudios
26     725      112     znatrainer
27    2507      112     evan
28    1241      110     crystal
29    2408      108     dudeman718
30     594      107     tweetmeme
31    2715      105     th
32    2658      101     tiger
33    1815       99     spam
34    2941       98     ded
35    3510       97     drewfromtv
36    1386       94     lukester
37    2604       94     al
38     460       92     americanwomannn
39     726       92     stop
40    4696       92     nytimes

and here the network graph:

From Drop Box

it is obvious that the one all neighbourhood of the twitter team cobntains a lot of other important (non twitter-team list) accounts that are partly more important than members of the twitter list themselves. Althoug derived from the twitter team list, the twitter team doesn’t play a main part but only a peripheral role. (they do the work others tweet about it
unfortunately I cannot extract so far a network with members of the twitter list only.

stop, there’s a work around:
I exctract the surrounding network, but don’t inlcude messages between neighbours
by unchecking “Connect Nodes”

——————————–
1 1771 1011 ev
2 3771 861 biz
3 2468 291 delbius
4 833 289 al3x
5 2046 245 caroline
6 1273 207 nk
7 4740 199 charles
8 944 196 jess
9 1341 180 laura
10 466 156 twitterapi
11 2510 112 evan
12 1235 110 crystal
13 2714 105 th
14 2231 101 tiger
15 2943 98 ded
16 1385 94 lukester
17 2607 94 al
18 4347 92 stop
19 1818 90 rael
20 4989 83 vl
21 5141 82 kevinweil
22 3862 79 bs
23 3322 76 goldman
24 4681 72 rsarver
25 1750 63 magnuson
26 3271 62 joshelman
27 3044 61 dougw
28 2110 57 sean
29 5161 55 trammell
30 1923 54 sam
31 1739 50 stevej
32 4646 50 ablegrape
33 4234 50 noradio
34 2450 49 lauraigomez
35 4243 49 che
36 4544 44 kevinthau
37 2587 43 edgutman
38 1925 42 anm
39 4102 42 mutgoff
40 237 39 netik

all top 40 are accounts from http://twitter.com/twitter/team

here’s how this network looks like:

From Drop Box

May 2010: now it is very easy to follow conversations among the twitter list members our brand new tool:

http://tlx.mememapper.com/#twitter/team

for more info how this analysis was done go to:
Community Core Detection in Twitter - a “Bottom Up” Heuristic

available at http://code.google.com/apis/chart/

Today i travel to Brussls and I’ll be [there]

We wrote an article for the book “Learning Communities. Das Internet als neuer Lern- und Wissensraum” published by Christina Schachtner, Angelika Höber at Campus. More info about the book can be found at Campus and Amazon.

Our contribution about “Visualising information flows in self organising knowledge networks” and can be found [here]. (in German)

Many Eyes is an easy to use data visualisation tool. First you upload data in the form of a spreadsheet, then you define forms of visualisations and finally customise and publish it. Many Eyes offer a dozen different visualisation types (maps, graphs, charts, histograms, and even network diagrams) and it is possible to apply a multitude of different forms to the same data set; Thus it is possible to play around with figures and to discover and highlight different aspects within the same data set. It is easy to integrate a thumbnail of a visualisation in your blog that links to the correspondent page at Many Eyes.
A thumbnail is by definition not rich in details and therefore you need to click on it in order to see the actual information. In the first moment one might prefer having the actual visualisation in the own weblog, but Many Eyes is not a mere visualisation platform but also a forum for interpretation. People start to discuss data like in this case. And that’s actually the greatest benefit of the service. It combines state of the art visualisation with the blessings of the living web.
I doubt that the masses will start discussing data sets but I think there’s a general tendency to explore global interdependencies. In a “globalised world” figures seem to be the most concrete evidence for developments, because in most cases we don’t have a first hand experience. Therefore statistics and network analysis seem to be an appropriate and necessary means of developing new forms of perception and discourse in order to cope with actually invisible but nevertheless vital challenges. I hope that XXx and XXX will go in with their approach.

http://services.alphaworks.ibm.com/manyeyes/view/S2fqLEsOtha64-EgR_rLE2-

Thanks Dietmar Offenhuber and Gerhard Dirmoser from SemaSpace for sending me the link to Many Eyes!

This is the kind of democracy we would really need: First present the facts, Second discuss it in all aspects. And third make a decision on it.

By this summer two applications, using Facebooks API, have launched. They can be seen as the first field test of social network analysis within a broader audience of non-technical users. Apart from the actual design of the applcation, the reactions of the users, often being in first contact with social network analysis are interesting lessons to learn.

Design: First of all one has to state that both TouchGraph (TG) and the Interactive Friends Graph (IFG) seem to be largely inspired by Danah Boyd’s and Jeffrey Heer’s prototype Vizster. That’s not a critic, as vizster was a proper design addressing most of the questions a user might ask her/himself when browsing her/his social network. It’s wise to reuse design that has proven itself. Clustering and displaying common friends are logical usecases that have been already covered by Vizster.
The limited number of use cases – especially for non-scientific users – and the grammar of network graphs defined by nodes, edges and proven layout algorithm make it almost impossible to reinvent the wheel. So to a certain extent network graphs cannot avoid looking similar and using a common language.

Common problems: Both Applications suffer to a large extent from the fact that profile informations are largely not public within facebook. Even if your friends allow you to see their friends, connections between them easily disappear, as the path simply ends when a user’s profile is hidden. The Interactive Friends Graph tries to overcome this problem by some sort of viral marketing strategy: It has a special function to invite friends to publish their friendship relations in the form of the IFG. This may work out to a certain extent but nevertheless it remains a barrier for a user that want to explore a persons network. Touchgraph tries to solve the problem with another trick: It takes advantage of photo tagging what allows you to display networks of people tagged on the same photo.
“One can not see another person’s whole social network because Facebook only allows applications to get a list of one’s own friends. For other users it is only possible to get a list of people that they appear in photos with. Perhaps Facebook’s policy will change in the future.”

If you are lucky by having a large Facebook network of friends then Touchgrouph provides a wonderful tool to explore all the connections between your friends.

Metrics: The policy of ‘Facebook makes sense because it protects people from being stalked but it has negative impacts to the application of metrics. One must consider that the probability of being cut off raises exponentially with each degree of separation from a central point, simply because of the fact that the connecting point might not be within your network.
“Once one has launched the application, one can explore one’s extended social network by loading more photos for friends. Loading photos will add new users who are tagged in photos to the graph, and created edges between them based on friendships and common photo appearances. Note: It is only possible to load photos for friends and people within one’s network.”
Its simply ridiculous that facebook applies the term network to people of the same nationality or the same university and provides far more information for people within than for people outside of one’s network. There’s a structural barrier for becoming friends with people outside your own network and therefore any metrics will only affirm these restrictions.

Or lets put it in other words: The ways how blogs link in the blogosphere is far more inpredictable, because its easier to escape from national and other social ties whereas facebook structurally supports friendship connections within the own neighbourhood. One of the main benefits of network graphs seems to lie in providing a tool to go beyond your own neighbourhood and taking advantage of a weak tie between an important actor and yourself.
Within Facebook such explorations are rather hindered than supported.

Nevertheless the Touchgraph Facebook Photo Browser is certaily among the best WORKING social network analysis tool currently available for online usage. It has variety of fascinating and usefull features and is worth being tried out - if you have large facebook network.

Einladung zur zweiten deutschen Konferenz für Informationsarchitektur, die am 9. und 10. November 2007 unter dem Titel „Information Raum geben“ bei der Hochschule der Medien, Stuttgart (HdM) zu Gast ist. Die diesjährige Konferenz bildet gleichzeitig das Programm des 6. Symposiums für Informationsdesign und wird gemeinsam vom Studiengang Informationsdesign an der HdM und dem Institut für Informationsarchitektur veranstaltet.

mehr infos [hier]