These are the connections among the Twitter users who recently tweeted the word #JW11 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.jivesoftware.com/jiveworld

A larger version of the image is here: www.flickr.com/photos/marc_smith/6234653454/sizes/l/in/ph…

Top most between users:
@jivesoftware
@gialyons
@mikefraietta
@cflanagan
@cosmopolitan_lv
@ginorossi
@kristinhersant
@thebrandbuilder
@alanlepo
@mor_trisha

Graph Metric: Value
Graph Type: Directed
Vertices: 345
Unique Edges: 3606
Edges With Duplicates: 2072
Total Edges: 5678
Self-Loops: 632
Connected Components: 11
Single-Vertex Connected Components: 9
Maximum Vertices in a Connected Component: 334
Maximum Edges in a Connected Component: 5659
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.353001
Graph Density: 0.034184361
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

http://ir12.aoir.org/

These are the  connections among the Twitter users who recently tweeted the word AOIR when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: ir12.aoir.org/

Top most between users:
@ir12
@barrywellman
@mikemonello
@netcrit
@andresmh
@s_t_e_v_e_jones
@zizip
@kellybergstrom
@katypearce
@guillaumelatzko

Graph Metric: Value
Graph Type: Directed
Vertices: 98
Unique Edges: 119
Edges With Duplicates: 672
Total Edges: 791
Self-Loops: 153
Connected Components: 40
Single-Vertex Connected Components: 35
Maximum Vertices in a Connected Component: 55
Maximum Edges in a Connected Component: 710
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.257477
Graph Density: 0.035766884
NodeXL Version: 1.0.1.179

By expanding the query to include #IR12, the conference hashtag, the network expands to include:

Connections among the Twitter users who recently tweeted the word AOIR OR #IR12 when queried on October 11, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Top most between users:
@ir12
@barrywellman
@mikemonello
@netcrit
@andresmh
@s_t_e_v_e_jones
@zizip
@kellybergstrom
@katypearce
@guillaumelatzko

Graph Metric: Value
Graph Type: Directed
Vertices: 231
Unique Edges: 1984
Edges With Duplicates: 2118
Total Edges: 4102
Self-Loops: 586
Connected Components: 46
Single-Vertex Connected Components: 40
Maximum Vertices in a Connected Component: 181
Maximum Edges in a Connected Component: 4012
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.213887
Graph Density: 0.046320346
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Link: www.flickr.com/photos/marc_smith/6225446144/

Each user is scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: occupywallst.org/

A larger version of the image is here: www.flickr.com/photos/marc_smith/6225446144/sizes/l/in/ph…

Top most between users:
@maddow
@motherjones
@aclu
@zerohedge
@mmfa
@spread4freedom
@diggrbiii
@angelsavant
@marychastain
@katiepavlich

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3835
Edges With Duplicates: 916
Total Edges: 4751
Self-Loops: 1109
Connected Components: 311
Single-Vertex Connected Components: 301
Maximum Vertices in a Connected Component: 678
Maximum Edges in a Connected Component: 4327
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.283708
Graph Density: 0.003408408
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

The Future of Technology in Education workshop met on 7 October 2011 in London at the University of London Computer Centre (ULCC) to discuss the ways technology is changing the learning experience.

The hashtag for the event is #FOTE11 so I made a NodeXL SNA Map -

The following is a map of the connections among the Twitter users who recently tweeted the word #fote11 when queried on October 7, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: fote-conference.com/wordpress/

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6221247656/sizes/l/in/ph…

Top most between users:
@fotie
@jamesclay
@timbuckteeth
@hopkinsdavid
@dajbelshaw
@eingang
@fredgarnett
@pressfuturist
@grahambm
@eyebeams

Graph Metric: Value
Graph Type: Directed
Vertices: 251
Unique Edges: 2904
Edges With Duplicates: 1908
Total Edges: 4812
Self-Loops: 730
Connected Components: 7
Single-Vertex Connected Components: 6
Maximum Vertices in a Connected Component: 245
Maximum Edges in a Connected Component: 4803
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.246839
Graph Density: 0.052988048
NodeXL Version: 1.0.1.179

The data set for this network is available from the NodeXLGraphGallery.

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

Connections among the Twitter users who recently tweeted the word Oracle when queried on October 5, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Top most between users:
@benioff
@oracleopenworld
@salesforce
@arstechnica
@sai
@oracledatabase
@sapjobswithace
@kirbylunger
@capgemini
@reuters_biz

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 2747
Edges With Duplicates: 827
Total Edges: 3574
Self-Loops: 1102
Connected Components: 425
Single-Vertex Connected Components: 396
Maximum Vertices in a Connected Component: 541
Maximum Edges in a Connected Component: 2923
Maximum Geodesic Distance (Diameter): 10
Average Geodesic Distance: 3.475208
Graph Density: 0.00226026
NodeXL Version: 1.0.1.179

A related map represents the connections among people who tweeted the term #OOW11

These are the connections among the Twitter users who recently tweeted the word oow11 when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.oracle.com/openworld/live/on-demand/index.html

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@oracleopenworld
@oracle
@oracletechnet
@rwang0
@devops_borat
@salesforce
@mysql
@emcoracle
@oraclemix
@oracledatabase

Graph Metric: Value
Graph Type: Directed
Vertices: 670
Unique Edges: 5882
Edges With Duplicates: 1573
Total Edges: 7455
Self-Loops: 881
Connected Components: 45
Single-Vertex Connected Components: 44
Maximum Vertices in a Connected Component: 626
Maximum Edges in a Connected Component: 7405
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.770009
Graph Density: 0.013401602
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

Orange is a major European telecommunications provider that has been focused on what they call the “porous enterprise” – organizations in which many of the previous boundaries and barriers between businesses and customers are gone.  Social media flows in and out of companies and mixes with public collections of discussions about them. Locked down corporate laptops now are joined by employee owned mobile devices.  Consumer social media products now mingle with enterprise social media.  Corporations now engage directly with customers through public consumer social media services.

For example, Orange has several Twitter accounts.  @Orange provides general product information while @Oranger_Conseil and @OrangeHelpers provide customer support for French and English speaking customers, and @orangeapi offers technical information for developers building applications against services offered by Orange.  There are several other related accounts.

The @Orange account has 2,039 followers and is following 621.  It has 673 tweets.  The pattern of connections among these people emerges into a network that can be clustered into sub-groups:


These are the connections among the Twitter users who follow or are followed by Orange when queried on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users follow one another.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6209515710/sizes/l/in/ph…

Top most between users:
@orange
@orangebusiness
@twitter
@ygourven
@pressecitron
@frenchweb
@lionelfumado
@sosh_fr
@lemondefr
@mashable

Graph Metric: Value
Graph Type: Directed
Vertices: 1587
Unique Edges: 18724
Edges With Duplicates: 26647
Total Edges: 45371
Self-Loops: 0
Connected Components: 229
Single-Vertex Connected Components: 227
Maximum Vertices in a Connected Component: 1358
Maximum Edges in a Connected Component: 45367
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.625415
Graph Density: 0.012709666
NodeXL Version: 1.0.1.179

The dense follows/follower map is a network that represents potential communication, the links indicate that there is a “follows” relationship between any two people.  The network of activated connections created when people reply or mention one another is more sparse.  For example, the @Oranger_Conseil and @OrangeHelpers accounts get mentioned by a number of other users who interact with them.  The connections among these people creates a network pattern:

This is a map of the connections among the Twitter users who recently tweeted the word Orange conseil OR OrangeHelpers when queried on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

It is characterized by a hub and spoke pattern created as customers who have few connections to one another are linked to one of the hub customer service accounts for English and French speakers.

Top most between users:
@orangehelpers
@orange_conseil
@orange
@thomaslegac
@conorfromorange
@orangeripoff
@presseorange
@orangecomplaint
@lisepressac
@poupimali

Graph Metric: Value
Graph Type: Directed
Vertices: 372
Unique Edges: 474
Edges With Duplicates: 3308
Total Edges: 3782
Self-Loops: 73
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 371
Maximum Edges in a Connected Component: 3781
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 2.356011
Graph Density: 0.008361592
NodeXL Version: 1.0.1.179

Mentions of these accounts are good indications that the topic is the Orange Telecom company and not the many other Orange entities (like Orange County, the fruit, and the color, among others).  Searching for the term “Orange” or even “#Orange will likely bring back a large amount of these “name-space collisions” – overlapping uses of the term “Orange”.

These are the connections among the Twitter users who recently tweeted the word #Orange when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

This network of connections among the population of people who tweeted the term “#Orange has many “isolates” – users who do not follow, reply or mention any other person in the network.  These people all tweeted “#Orange” but they lack any connection to anyone else who did so.

The large cluster of connected users is a group of people discussing the Orange Telecom company, while the other clusters involve people discussing the colors of Autumn (Pumpkins!).

Top most between users:
@bluetouff
@laouffir
@fbrahimi
@presseorange
@damiendouani
@gregfromparis
@eogez
@thomaslegac
@isabellespanu
@challenges

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 2282
Edges With Duplicates: 960
Total Edges: 3242
Self-Loops: 1170
Connected Components: 584
Single-Vertex Connected Components: 515
Maximum Vertices in a Connected Component: 155
Maximum Edges in a Connected Component: 1123
Maximum Geodesic Distance (Diameter): 8
Average Geodesic Distance: 2.787863
Graph Density: 0.001827828
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

The Workshop on Information in Networks 2011 was held at NYU September 30-October 1st.

Over the course of the event a number of people tweeted using the hashtag #win2011.

These are the connections among the Twitter users who recently tweeted the word win2011 when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: winworkshop.net/

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here:http://www.flickr.com/photos/marc_smith/6209064926/sizes/l/in/photostream/

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:

@sinanaral
@winworkshop
@barrywellman
@ladamic
@seanjtaylor
@drewconway
@chrisdiehl
@dylanwalker
@marc_smith
@ariegoldshlager

Graph Metric: Value
Graph Type: Directed
Vertices: 38
Unique Edges: 227
Edges With Duplicates: 193
Total Edges: 420
Self-Loops: 68
Connected Components: 1
Single-Vertex Connected Components: 0
Maximum Vertices in a Connected Component: 38
Maximum Edges in a Connected Component: 420
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.836565
Graph Density: 0.194167852
NodeXL Version: 1.0.1.179

These are the connections among the Twitter users who tweeted the word win2011 when queried a few days earlier on September 30, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6198785387/sizes/l/in/ph…

Top most between users:
@sinanaral
@winworkshop
@ariegoldshlager
@drewconway
@chrisdiehl
@kateaanderson
@dylanwalker
@barrywellman
@kristinalerman
@pboothe

Graph Metric: Value
Graph Type: Directed
Vertices: 16
Unique Edges: 60
Edges With Duplicates: 45
Total Edges: 105
Self-Loops: 26
Connected Components: 1
Single-Vertex Connected Components: 0
Maximum Vertices in a Connected Component: 16
Maximum Edges in a Connected Component: 105
Maximum Geodesic Distance (Diameter): 3
Average Geodesic Distance: 1.617188
Graph Density: 0.270833333
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

At the end of the conference on October 5th, the network of connections among the people who tweeted about Wikisym looked like this:

These are the connections among the Twitter users who recently tweeted the word wikisym when queried on October 5, 2011, scaled by numbers of followers (with outliers thresholded). Connections are created when users reply, mention or follow one another.

See: www.wikisym.org/

Top most between users:
@edchi
@jfelipe
@phauly
@readermeter
@hfordsa
@wikisym
@clifflampe
@staeiou
@geoplace
@wikimedia

Graph Metric: Value
Graph Type: Directed
Vertices: 170
Unique Edges: 1163
Edges With Duplicates: 2165
Total Edges: 3328
Self-Loops: 658
Connected Components: 5
Single-Vertex Connected Components: 3
Maximum Vertices in a Connected Component: 165
Maximum Edges in a Connected Component: 3319
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.385502
Graph Density: 0.052941176
NodeXL Version: 1.0.1.179

In contrast, a few days earlier, these are the connections among the Twitter users who recently tweeted the word wikisym when queried on October 3, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

Top most between users:
@wikisym
@jfelipe
@andicat
@geoplace
@staeiou
@dirkriehle
@wikimedia
@edchi
@bkeegan
@hfordsa

Graph Metric: Value
Graph Type: Directed
Vertices: 78
Unique Edges: 459
Edges With Duplicates: 354
Total Edges: 813
Self-Loops: 132
Connected Components: 10
Single-Vertex Connected Components: 8
Maximum Vertices in a Connected Component: 68
Maximum Edges in a Connected Component: 800
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.207506
Graph Density: 0.086913087
NodeXL Version: 1.0.1.179

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

Marc Smith on Twitter.

The recent Strata Conference in New York City gathered many leaders in big data, data mining, and information visualization.

Link: www.flickr.com/photos/marc_smith/6203313170

Connections among the Twitter users who recently tweeted the word Strata when queried on September 20, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: strataconf.com/summit2011

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6203313170/sizes/l/in/ph…

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@timoreilly
@strataconf
@doctorow
@acroll
@ticbeat
@datacenter
@hnshah
@oreillymedia
@markmadsen
@unglobalpulse

Graph Metric: Value
Graph Type: Directed
Vertices: 1118
Unique Edges: 5813
Edges With Duplicates: 6522
Total Edges: 12335
Self-Loops: 1010
Connected Components: 9
Single-Vertex Connected Components: 8
Maximum Vertices in a Connected Component: 1110
Maximum Edges in a Connected Component: 12327
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.575622
Graph Density: 0.00647018
NodeXL Version: 1.0.1.177

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

This is a map of the connections among the Twitter users who recently tweeted the word HealthTap when queried on September 28, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: healthtap.com/

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6198785341/sizes/l/in/ph…

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Clauset-Newman-Moore algorithm is defined here: pre.aps.org/abstract/PRE/v70/i6/e066111

Top most between users:
@lifehacker
@healthtap
@drpam4women
@androidandme
@drpam
@amazonwireless
@jonmrich
@etseng
@mobilehealth
@doctor_v

Graph Metric: Value
Graph Type: Directed
Vertices: 638
Unique Edges: 3630
Edges With Duplicates: 1337
Total Edges: 4967
Self-Loops: 865
Connected Components: 183
Single-Vertex Connected Components: 167
Maximum Vertices in a Connected Component: 426
Maximum Edges in a Connected Component: 4677
Maximum Geodesic Distance (Diameter): 11
Average Geodesic Distance: 3.098025
Graph Density: 0.008715423
NodeXL Version: 1.0.1.177

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

This is a map of the connections among the people who tweeted the terms “FOCAS11″ or “Aspen Institute” on August 2, 2011.

Connections among the Twitter users who recently tweeted the word Aspeninstitute OR FOCAS11 when queried on August 2, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.aspeninstitute.org/policy-work/communications-society…

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6001893675/sizes/o/

Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality

Top most between users:
@aspeninstitute
@lancearmstrong
@emptywheel
@knightfdn
@pkedrosky
@bostonreview
@josefjohann
@kgosztola
@utknightcenter
@sowers

Graph Metric: Value
Graph Type: Directed
Vertices: 324
Unique Edges: 649
Edges With Duplicates: 743
Total Edges: 1392
Self-Loops: 339
Connected Components: 81
Single-Vertex Connected Components: 76
Maximum Vertices in a Connected Component: 240
Maximum Edges in a Connected Component: 1284
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.67739
Graph Density: 0.007023277
NodeXL Version: 1.0.1.173

More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

This is a map of the connections among the Twitter users who recently tweeted the words #rw2011 when queried on April 28, 2011, scaled by numbers of followers (with outliers thresholded), connected by replying, mentioning and following.

Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.

A larger version of the network map is here: www.flickr.com/photos/marc_smith/5666637994/sizes/o/

A detailed list of top most between users is here: www.flickr.com/photos/marc_smith/5666069249/

Top most between users: @nytimes, @skynews, @peoplemag, @royalweddingcnn, @itv_news, @katewilliam2011, @julieetchitv, @abc, @cbsnews, @abcroyals

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 1735
Edges With Duplicates: 531
Total Edges: 2266
Self-Loops: 0
Connected Components: 271
Single-Vertex Connected Components: 246
Maximum Vertices in a Connected Component: 701
Maximum Edges in a Connected Component: 2199
Maximum Geodesic Distance (Diameter): 11
Average Geodesic Distance: 3.892905
Graph Density: 0.001984985
NodeXL Version: 1.0.1.166

NodeXL is free and open and available from www.codeplex.com/nodexl

NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.

The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.

Lists are a recent feature of Twitter which enable users to compile collections of users to follow in a single tweet stream.  People can add up to 500 people to follow on a single list.    People on a list may be connected to one another if one follows the other.

Some people on a list may have many connections.  Some have only a few or even no connections to others on the list.

In version 161 of NodeXL you may now create maps of the connections among a list of Twitter users.

There are two options in this feature.  One makes use of the List functions in Twitter.  If you request the map for a single twitter list, NodeXL will build a map of the connections among all the people Twitter reports as being on the list.  You can create and manage the people on a list using the Twitter list features, or select an existing Twitter list created by other users.

A second option in this feature accepts a list of up to 10,000 twitter user names pasted into the query text box.  If you have a list of users and want a map of how they are connected, and the list is not already in Twitter, just paste them here and get a map.

Either way, a connection will be created for every two users if one follows the other.

Here is an example of the network map of the Twitter list of social network analysis people maintained by Valdis Krebs: valdiskrebs\network-analysts

Here is a recipe for finding and building collections of influential Twitter users relevant to your goal and message.

A common question related to social media that I am often asked is: “how do we grow our twitter followers?”  I try to re-frame that question as “how can we get our message viewed by the right people?” and its related question: “who could best pass our message along?”

The goal is not to gain many followers, but rather to gain well positioned followers!  A well positioned follower is someone who talks about topics related to your topic and is located at the center of many relationships with other people in that topic network.  These highly central people are able to get their message to be visible to many people. If they re-tweet you, your message will go far.  Why would they re-tweet you?  First, they have to be able to hear you to re-tweet you and that means that you need to get these people to follow you.  Second, you need to say something sensible enough to be worth re-tweeting.

The task is finding the important users to focus on.  To find them, create a network map of the relevant topics.  A few people in these network maps will occupy key positions in which they have many connections or have special ties that bridge gaps between sub-groups.  There are often only a few of these people in any topic area.

Find these strategically located people and follow them and often they will follow you back. A good proxy for the strategic value of a person’s location is their “betweenness centrality” – a measure of how much a person acts a bridge between others.

If the people you select and follow do not immediately follow you back there is another opportunity to get them to follow you.  If you re-tweet particularly good content from the people you have selected to follow there is a good chance that you will be followed back.

Once you have identified, followed, and been followed back by some fraction of the influential people around your topics of interest you have a increased chance that your tweets will be re-tweeted.  For each re-tweet, your message is exposed to a potentially large and highly targeted group of people.  Over time it is possible to create a relationship with a collection of highly influential participants who may be interested in sharing your message with their followers.

The event is a globally visible opportunity for the President of the United States to present the Administration’s vision for its agenda for the year to the people of the US and the world.

The hashtag #stateoftheunion is already very active in Twitter.

This is a NodeXL map of the connections among people who recently tweeted the string “stateoftheunion” on 21 January, 2011.

The most between user, @keitholbermann, is highlighted.  Two major clusters emerge, shown arbitrarily here as green and blue, and which are more clearly seen in the filtered graph below the list.

Summary network statistics:

Graph Type Directed
Vertices 1259
Unique Edges 8014
Edges With Duplicates 544
Total Edges 8558
Connected Components 306
Single-Vertex Connected Components 293
Maximum Vertices in a Connected Component 937
Maximum Edges in a Connected Component 8513
Maximum Geodesic Distance (Diameter) 9
Average Geodesic Distance 3.140191
Graph Density 0.00522786

The most “between” contributors in the network are:

@keitholbermann, @borowitzreport, @thefix, @katyinindy,
@america1first, @wbconservative, @westwingreport, @dailykos,
@writer2go, and @gottalaff

When contributors with low “betweenness” are filtered out of the graph, two dominant clusters become easier to discern.  To the left are supporters of the President and to the right are those critical of the Administration.  A representative tweet from this cluster is highlighted.

A recent paper “EventGraphs: Charting Collections of Conference Connections” by Marc Smith from Connected Action, Professor Derek Hansen (College of Information Studies) and Professor Ben Shneiderman (Computer Science/Human Computer Interaction Lab) both from the University of Maryland and has been accepted for publication at the 2011 Hawaii International Conference on System Sciences (HICSS) Conference.  This is the 44th year for the conference. Derek Hansen presented the paper on January 7, 2011.

Hansen, D., Smith, M., Shneiderman, B., EventGraphs: charting collections of conference connections. Hawaii International Conference on System Sciences. Forty-Forth Annual Hawaii International Conference on System Sciences (HICSS). January 4-7, 2011. Kauai, Hawaii.

http://www.cs.umd.edu/localphp/hcil/tech-reports-search.php?number=2010-13

Our paper is about visualizing social media and it describes the visualization of the patterns of connections formed when people tweet about events like conferences and news stories.

EventGraphs are social media network diagrams constructed from content selected by its association with time-bounded events, such as conferences. Many conferences now communicate a common “hashtag” or keyword to identify messages related to the event. EventGraphs help make sense of the collections of connections that form when people follow, reply or mention one another and a keyword. This paper defines EventGraphs, characterizes different types, and shows how the social media network analysis add-in NodeXL supports their creation and analysis. The paper also identifies the structural and conversational patterns to look for and highlight in EventGraphs and provides design ideas for their improvement.

A collection of EventGraphs are available in flickr: http://www.flickr.com/photos/marc_smith/sets/72157625618025980/

The EventGraph for HICSS this year is here: 20110109-NodeXL-Twitter-HICSS

The top most between contributors to the HICSS Twitter graph this year are: @arcticpenguin @avantgame @barrywellman @clifflampe @dalprof @chandlerism @shakmatt @marc_smith @shen045 and @thecatandthekey

Here is the data set: 20110109-NodeXL-Twitter-HICSS
Graph Metric Value
Graph Type Directed
Vertices 92
Unique Edges 243
Edges With Duplicates 71
Total Edges 314
Self-Loops 0
Connected Components 21
Single-Vertex Connected Components 18
Maximum Vertices in a Connected Component 69
Maximum Edges in a Connected Component 307
Maximum Geodesic Distance (Diameter) 8
Average Geodesic Distance 3.081693
Graph Density 0.032967033

NodeXL Version 1.0.1.159

An article about social media visualization Social Seen: Analyzing and Visualizing Data from Social Networks by Hunter Whitney, appeared in UX Magazine on December 15, 2010.  It provides a great round up of tools and approaches to mapping populations in social media spaces.

Previous HICSS papers:

Gleave, Eric, Howard T. Welser, Marc Smith, and Thomas Lento.  2009.  “A conceptual and operational definition of social role in online community.”   In Proceedings of the 42nd  Hawaii International Conference on Systems Sciences (HICSS), January 5-8. Computer Society Press.  (Best Paper, Digital Media and Communication Mini-Track)

2009 – HICSS – 42 – Best Paper – A Conceptual and Operational Definition of ‘Social Role’ in Online Community

Fisher, D., Smith, M., and Welser, H. You Are Who You Talk To, Proceedings of HICSS, January 2006. (Best Paper, Digital Media and Communication Program)

http://www.connectedaction.net/wp-content/uploads/2009/08/2006-HICSS-You-Are-Who-You-Talk-To-Detecting-Roles-in-Usenet-Newsgroups.pdf

Viégas, Fernanda B., Marc Smith. “Newsgroup Crowds and AuthorLines: Visualizing the Activity of Individuals in Conversational Cyberspaces“, Proceedings of Hawaii International Conference on Software and Systems (HICSS) 2004. (Best Paper: Persistent Conversation Minitrack)

http://www.connectedaction.net/wp-content/uploads/2009/08/2004-HICSS-Viegas-and-Smith-Newsgroup-Crowds-and-Author-Lines.pdf

Here are some sample event graphs:

As mobile devices become a major method for authoring and consuming social media, location data is increasingly a part of many posts, tweets, check-ins, and messages.  Many Twitter clients, for example, can add the user’s current latitude and longitude to the metadata associated with a tweet.  Other systems like Facebook Places, Google Latitude and Foursquare encourage users to declare  where they are to the world, often passing the information to other social media sites.

Using this location data in network analysis opens up a range of new opportunities.  Instead of a person – to – person social network, location data allows people to be linked to places and, by extension, places can be linked to other places based on the patterns of connection people create when located in a particular place.  A convergence of network analysis and Geographic Information Systems in underway.  A great example of this can be found in this wonderful video from the BBC which demonstrates the idea by mapping the flow of telephone calls, texts, and data around the UK and the wider world.

Link on the BBC

Even better is this video from the SensibleCity group at MIT:

Now, NodeXL (v.156) has the first of a series of features that will start to approximate the experience displayed in the video by supporting the import of location data about networks and plotting networks onto maps.

For now, we have started importing latitude and longitude data that Twitter makes available.  If you check “Add a Tweet column to the Vertices worksheet” in NodeXL, Data, Import, From Twitter Search Network or From Twitter User Network, the Twitter user’s geographical coordinates will be added to the Vertices worksheet when they are available.

Note that not every tweet has a latitude and longitude, in fact many do not (yet).  Further, note that not every latitude and longitude is accurate, many are not.

We need to implement more features for better location data support in a NodeXL workbook, but this is a start.  We are interested in exploring geospatial networks and Twitter is a great data source.  With this data in place we may look into features that emit KML files for exploration in other packages like Google Earth.  A nifty Google Earth/Spreadsheet importer can take small sets (400) of location data points in a spreadsheet and export them to a KML file, something we could implement in the future as well.  In addition we may be able to connect directly with services like Bing Maps and Google Maps to display connections between nodes with known locations.  Metrics that calculate the distance between nodes seem sensible as well.

Location coordinates are the key to a cornucopia of related data about a place.  Given a latitude and longitude it is possible to find the name of the city it is located in, look up data about that location in official records as well as resources like Wikipedia.  Income, education, property values, weather, photos, and more can be pulled together from just a simple lat/long.  All of these attributes could be used to cluster or illustrate the network visualization.

Here are some NodeXL generated maps of recent  discussions around Wikileaks, pdfleaks, and cablegate in Twitter:

These are the connections among the people who tweeted the term “wikileaks” on 11 December 2010. The “wikileaks” account is at the center of the inner circle.  It is surrounded by a collection of very lightly interconnected people (there is a low clustering coefficient) who retweet messages from the wikileaks account.  This ring is surrounded by a thinner ring of 2nd order retweets.  This is a very common pattern for a successful “brand” or broadcast account, getting lots of retweets and, even more impressive, retweets of retweets.

The most between people who tweeted the term “wikileaks” on 11 December 2010.  The top ten: @wikileaks, @melhoresfrases, @ukelectionnews, @nonradical, @nelsonbocaranda, @linuxmall, @dominiofeminino, @resermarca, @crazy_dymnd, @rodolfomoros

Here is the data file: 20101209-NodeXL-Twitter-wikileaks

These are the connections among people who recently tweeted “pdfleaks” in Twitter on 11 December 2010.  It shares the brand/broadcast structure of the wikileaks account shown above.  After the wikileaks account itself, the second most between participant is @jeffjarvis who uniquely bridged to many people who would otherwise not be connected in this graph.

The most between people who tweeted the term pdfleaks“ on 11 December 2010.

Here is the data set: 20101211-NodeXL-Twitter-pdfleaks

The most between people who tweeted the term “cablegate” on 11 December 2010.  The cablegate discussion is more multipolar, with several high betweenness hubs connecting to otherwise separate communities.  El Pais, for example, is reaching a mostly Spanish language community.  The top ten: @anonops, @el_pais, @mparent77772, @wllawyer, @wikileaks_pp, @gdncables, @milenio, @evgenymorozov, @muschelschloss, @saladeprensa.

Here is the data set:

I spoke at the University of Michigan, School of Information on October 19th, 2010 about “Charting Collections of Connections in Social Media: Mapping and Measuring Social Media Networks to Find Key Positions and Structures“.

I demonstrated the ways social network data sets can be extracted from social media services like Facebook, Twitter, email, YouTube, and flickr.  These network graphs can reveal information about the “shape” of the population in terms of the presence of sub-groups and communities within the larger population.  In addition, each individual participant is located or positioned within the graph, helping to identify the people who are “core” versus those who are peripheral, as well as those who occupy the position of “bridge” between two otherwise separate groups.

Yahoo! Speaker Series

The Yahoo Speaker Series at the School of Information supports distinguished guest lecturers from the fields of information and technology.

2010 Speaker: Marc Smith
VIDEO: Marc Smith

Marc Smith, chief social scientist with the Connected Action Social Group, presented “Charting Collections of Connections in Social Media: Mapping and Measuring Social Media Networks to Find Key Positions and Structures,” on Tuesday, Oct. 19, 2010.

Smith’s talk was sponsored by the Yahoo! Speaker Series, Michigan Interactive & Social Computing, and the School of Information.

In this talk, Smith described how networks are a data structure common across all social media systems — systems defined by enabling populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. Using NodeXL, networks become as easy to create and analyze as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, Flickr, YouTube, and e-mail networks will be presented, illustrating different patterns created when communities, brands, and controversies are discussed.

I am coming to understand what is interesting in any particular network visualization by looking at many network visualizations.

Using NodeXL, I have made several maps of  social media networks of people talking about several topics of interest from current events to conferences I attend.  You can find a collection of them on flickr.

I look at these images and look for differences in the number of big clusters: some images have a “double yolk” – that I propose is a necessary (but not necessarily sufficient) condition of defining a topic to be “controversial”.  These two cluster networks have two well defined populations who lack much if any connection across the divide to the “other” side.

Some networks are highly populated but sparse, these are often the networks that form around brands where a central account tweets and is retweeted by many.  But these many lack much connection to one another.  So these brands form broadcast networks, not communities.

Some networks are dense single clusters with few if any “isolates”.  Isolates are people who say a term, and thus appear in the graph, but have no connections (follows, replies, ore mentions) to anyone else in the graph (at least as observed and reported by twitter at that time).  These dense clusters without isolates are topics where everyone is in-group.  Examples, like “scrm”, are technical and business terms that identify medium sized populations with high levels of density.

Have a look and see what patterns you can find.