In collaboration with Optimice, I will teach a workshop on Social Network Analysis for enterprises, organizations, and businesses using NodeXL.

• Self-paced e-learning (4 hours)
  • Introduction to Social/Organisational Network Analysis
  • Network patterns and metrics
  • Software tools for network analysis
  • Managing an ONA Project
    
• Module 1: Scoping your ONA Project (2 hour virtual session hosted by Patti Anklam)
  • Determining which business problem to solve with ONA
  • Review of case-studies
  • Determining your questions
• Module 2: Setting up your ONA survey (2 hour virtual session hosted by Cai Kjaer / Laurence Lock Lee)
  • Setting up your survey
  • Working with mailing lists and other lists
  • Creating relationship sets and network questions
  • Previewing and launching the survey
  • Tracking progress and downloading responses
• Module 3: Visualise networks with NodeXL (2 hour virtual session hosted by Marc Smith)
  • Getting started with NodeXL
  • Calculating and visualizing network metrics
  • Preparing data and filtering
  • Importing data from Social Media tools
  • Clustering and grouping

A number of ONA Practitioner Courses are available to suit the timezones of participants located in the US, Europe and/or Asia-Pacific (but not restricted to these regions):

I will speak this March 4th at the 2012 Predictive Analytics World in San Francisco about ” Crowd Photography for Social Media“.

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/speakers.php
http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#

Monday @ 5:25-5:45pm

Track 1:
Social Data Case Study:
Social Media Research Foundation
Crowd Photography for Social Media

Crowds of people gather in social media around many products, services, businesses, and events but they can be difficult to see and understand. With new free and open tools, it is now possible to map and measure social media spaces, capturing the sub-groups and key people within and between them. Learn how to capture social media data and quickly generate a visual map of the crowd. With maps in hand, we will discuss ways they guide a journey to the key influencers and concepts in the crowd.

Speaker: Marc Smith, Director, Social Media Research Foundation

I will present a talk at Personal Digital Archiving 2012 titled “Arc-chiving: saving social links for study“.

The conference will be held on Thursday-Friday, February 23-24, 2012 at the Internet Archive in San Francisco.

News and updates on the conference will be posted at the conference web site, http://personalarchiving.com.

My talk this year will focus on collecting and analyzing connections between digital objects (like users) and the insights these tools make possible.

Abstract: While digital content is archived in various ways, the “arcs” or links among people and their digital objects are not systematically saved. Efforts to store social media often overlooks including data about collections of connections. The Social Media Research Foundation is dedicated to open tools, open data, and open scholarship related to social media. It is producing tools that can collect, analyze and upload social media data, including the arcs that link people and objects. Using the free and open NodeXL application, users can collect, analyze and visualize complex networks and then upload the data to a growing archive on the web at NodeXLGraphGallery.org. As the group of researchers grows, an archive is being assembled to provide researchers around the world with the data about social media needed to understand the ways computer mediated communication tools shape society.

My talk at the 2011 Personal Digital Archiving conference is available through the Internet Archive’s video service:

Ines Mergel is my host.  Prof. Mergel is Assistant Professor of Public Administration, Department of Public Administration and International Affairs, and a Senior Research Associate at the Center for Technology and Information Policy at the Maxwell School of Citizenship and Public Affairs, Syracuse University, NY.

I will speak about the patterns we are finding in the data collected and analyzed by NodeXL.

Mastering Social Media 2011 is a workshop scheduled for November 28 and 30 in Cape Town and Johannesburg, South Africa.

My partner, Walter Pike, is a Marketing Maven and Founder of PiKE | New Marketing (www.pike.co.za) and the founder of the Digital Academy www.digitalacademy.co.za
He blogs at walterpike.com.

Mastering Social Media will give you practical tools on how to plan, execute and monitor your social media campaigns. Discussions will lead you through the introduction to social media marketing, understanding community dynamics, mapping social networks and applying network insights to your goals.
Brand Managers, Marketing Managers, Advertising Agencies, Digital Agencies, and PR Agencies are likely to find the day useful.

Venues and Dates

Johannesburg

30 November 2011
Gordon Institute
of Business Science,
Illovo

Pictures:

This map of connections among people who tweeted Teaparty starts on 11/15/2011 14:22 UTC and ends on 11/15/2011 17:23, a total of 3 hours and 1 minute of traffic.

The Teaparty data set contained 1,533 tweets, replies and mentions.
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.

Top most between users:
@ronpaul
@michellemalkin
@christopherhull
@theteaparty_net
@capaction
@thedailyedge
@bill1phd
@dbargen
@gulagbound
@rightcandidates

Graph Metric: Value
Graph Type: Directed
Vertices: 659
Unique Edges: 8808
Edges With Duplicates: 1423
Total Edges: 10231
Self-Loops: 1084
Connected Components: 49
Single-Vertex Connected Components: 44
Maximum Vertices in a Connected Component: 606
Maximum Edges in a Connected Component: 10148
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance:2.693965
Graph Density: 0.02036797
NodeXL Version: 1.0.1.193

The major clusters are composed of teaparty supporters. The center bottom cluster are teaparty critics.

This map of the connections among people who tweeted Occupywallstreet starts on 11/15/2011 23:08 and ends on 11/15/2011 23:34 UTC, a total of 26 minutes of traffic.

Occupywallstreet 1,370 tweets, replies and mentions
Blue edges are connections created by replies and mentions. Grey lines are follows relationships.

Top most between users:
@occupywallst
@mmflint
@nyclu
@allisonkilkenny
@andrewbreitbart
@operationleaks
@occupydenver
@theatlantic
@usgeneralstrike
@rt_com

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3546
Edges With Duplicates: 826
Total Edges: 4372
Self-Loops: 794
Connected Components: 241
Single-Vertex Connected Components: 230
Maximum Vertices in a Connected Component: 747
Maximum Edges in a Connected Component: 3998
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.65438
Graph Density: 0.003246246
NodeXL Version: 1.0.1.194

Some notable contrasts:
Teaparty Graph Density: 0.002652645
Occupywallstreet Graph Density: 0.02036797 – significantly lower levels of interconnection
Teaparty: Single-Vertex Connected Components 44 of 1000
Occupywallstreet: Single-Vertex Connected Components 283 of 1000

Many more “isolates” (Single-Vertex Connected Components) in Occupywallstreet.
Many more hubs, and more retweeting activity in Occupywallstreet.

The difference in duration of these data sets illustrates the relative speed of content creation in the topics. The data sets are commensurable in that they are both the result of a single query against the Twitter search API. So both maps are the results of charting connections among the authors of the last 1500 tweets, how ever long that takes to create.

Methodologies for Web and Social Media Data Analysis in Social Science and Policy Research

CCSR Short Course
Social Media Network Analysis using NodeXL

November 9th  9.00 am – 5.30 pm.

Marc Smith
Social Media Research Foundation
http://www.smrfoundation.org

Course Summary: Networks are everywhere in the natural and social world.  New tools are making the task of getting, processing, measuring, visualizing and gaining insights from network data sets easier than ever before.  The rise of social media offers a new and abundant source of network data.  The NodeXL project (http://www.codeplex.com/nodexl) from the Social Media Research Foundation (http://www.smrfoundation.org) offers a free and open path to network overview, discovery and exploration within the context of the familiar Excel spreadsheet.  In this short course we will introduce the NodeXL application and review the landscape of networks, social networks, and social media networks. Using the tool, non-programmers can quickly select a network of interest from various social media and other data sources.  Twitter, flickr, YouTube, email, the World Wide Web, and Facebook data can be quickly imported into NodeXL.  Networks can then be analyzed and visualized using tools similar to those used to create a pie chart or line graph [1].  As the challenge and cost of network acquisition and analysis drops, abundant data sets are being generated that document the range of variation of diverse sources of social media.  How many different kinds of Twitter hashtags exist?  Using snapshots of hundreds of hashtags collected over a year, it is now possible to build rough taxonomies of this kind of social media.  NodeXL provides access to a web gallery of data [2], allowing users to browse existing data sets and upload their own as well. Borrowing the vision of telescope arrays that create composite images far better than any individual instrument could, the Social Media Research Foundation envisions an user generated archive that provides a research asset that supports the collective effort to understand the structures and dynamics of network data.

[1] NodeXL Image Gallery: http://www.flickr.com/photos/marc_smith/sets/72157622437066929/
[2] NodeXL Graph Gallery: http://nodexlgraphgallery.org

Course Objectives
After this course, participants will:

(1) Be familiar with the basic concepts of networks, social networks and social media networks
(2) Understand the core features of the NodeXL network analysis and visualization tool
(3) Review images and data sets for dozens of different social media networks
(4) Learn to identify general types of social media networks along with the key people and groups within them

Target Audience
This course is suitable for people with some experience or interest in social media, social science, or social network analysis.  It is particularly appropriate for those who are involved in studying social structures and their change over time.

Laboratory and IT requirements:
Participants will need access to a computer connected to the Internet  and will be supplied with the free NodeXL software.

Suggested Reading
Analyzing social media networks with NodeXL: Insights from a connected world
http://www.amazon.com/gp/product/0123822297?ie=UTF8&tag=conneactio-20&linkCode=as2&camp=1789&creative=390957&creativeASIN=0123822297

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

Visualizing the Signatures of Social Roles in Online Discussion Groups:
http://www.cmu.edu/joss/content/articles/volume8/Welser/

Discussion catalysts in online political discussions: Content importers and conversation starters
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-JCMC-Discussion-Catalysts-Himelboim-and-Smith.pdf

Analyzing (Social Media) Networks with NodeXL
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-CT-NodeXL-and-Social-Queries-a-social-media-network-analysis-toolkit.pdf

Whiter the experts: Social affordances and the cultivation of experts in community Q&A systems
http://www.connectedaction.net/wp-content/uploads/2009/08/2009-Social-Computing-Whither-the-Experts.pdf

First steps to NetViz Nirvana: evaluating social network analysis with NodeXL
http://www.cs.umd.edu/~cdunne/pubs/Bonsignore09Firststepsto.pdf

]]> http://www.connectedaction.net/2011/11/05/november-8-2011-university-of-manchester-nodexl-sna-social-media-workshop/feed/ 0 http://www.connectedaction.net/2011/10/22/november-3-2011-seoul-south-korea-international-symposium-on-convergence-technology-contech-2011/?utm_source=rss&utm_medium=rss&utm_campaign=november-3-2011-seoul-south-korea-international-symposium-on-convergence-technology-contech-2011 http://www.connectedaction.net/2011/10/22/november-3-2011-seoul-south-korea-international-symposium-on-convergence-technology-contech-2011/#comments Sat, 22 Oct 2011 19:00:42 +0000 Marc Smith http://www.connectedaction.net/?p=4819

I will speak at the International Symposium on Convergence Technology (ConTech 2011) – Smart & Humane World – on November 3rd in Seoul, South Korea.

Date: 2011 November 3 (Thurs)
Place: COEX Grand Ballroom, Seoul, Korea
Organized by Advanced Institutes of Convergence Technologies (AICT), Seoul National University (SNU)
In Cooperation with Ministry of Knowledge Economy, Ministry of Education, Science and Technology, National Research Foundation of Korea, Graduate School of Convergence Science and Technology (GSCST)
Symposium Chair : Choi, Yanghee (President, AICT)

Program
09:00~09:30 Registration
09:30~10:00 Opening Ceremony
Plenary Session : Smart & Humane World through Convergence
10:00~10:40 Speaker (TBD)
10:50~11:30 Speaker (TBD)
11:30~13:00 Lunch
Session 1 : Bio Convergence (Chair : Prof. Kim, Sunghoon)
Session 2 : IT Convergence (Chair : Dr. Lee, Manjai)
Session 3 : Appropriate Technology (Chair : Prof. Kang, Namjun)
13:00~15:00 Scott A. Strobel (Professor, Yale University)
Speaker Kevin Kim (Professor, University of Illinois)
Speaker Masaru Kitsuregawa (Professor, Tokyo University)
Speaker Haesun Park (Professor, Georgia Institute of Technology)
Speaker Marc Smith (Connected Action)
Speaker Sang-goo Lee (Professor, Seoul National University)
Speaker Haklae Kim (Samsung)
Speaker Raghu Ramakrishnan (Yahoo)

My slides: 20111103 con tech2011-marc smith

I will also visit Professor Han Woo Park at YeungNam University (Wikipedia) in Daegu, South Korea to meet with his students in the Webometrics Institute program.

This will be my second trip to Korea, I was there last year for a related event.  Pictures after the jump:

I presented on social media network analysis on October 20, 2011 in New York City at Predictive Analytics World.

A map of the connections among the people tweeting about the #Pawcon hashtag is below.

Network Maps for End Users: Collect, Analyze, Visualize and Communicate Network Insights with Zero Coding

Abstract: Networks are everywhere except the end user desktop.  NodeXL, the free and open network overview, discovery and exploration add-in for the popular and familiar Excel (2007/2010) spreadsheet allows users who are comfortable making pie charts to now make useful network visualizations.  Developed and released by the Social Media Research Foundation, NodeXL uses Excel as a framework, providing a GUI network browser (a “web browser”?) that novices can use quickly and experts can use to generate sophisticated results.  Data importers provide access to a range of social media network data sources like Twitter, flickr, YouTube, Facebook, email, the WWW, and more through standard file formats (CSV, GraphML, Matrix).  Simple to use tools can automatically analyze, visualize and highlight insights in complex network graphs.  Using NodeXL, researchers have been collecting a wide range of network data sets from various social media services.  These images reveal a range of common social formations in social media and point to people who occupy strategic locations in these graphs.

This is a map of the connections among the people who tweeted the term “PAWCON” on the first day of the event:

These are the connections among the Twitter users who recently tweeted the word #pawcon when queried on October 19, 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/6261006732/sizes/l/in/ph…

Top most between users:
@tapan_patel
@pawcon
@sasanalytics
@deloitteba
@kristinevick
@jamet123
@zementis
@kdnuggets
@tibcospotfire
@saspublishing

Graph Metric: Value
Graph Type: Directed
Vertices: 41
Unique Edges: 233
Edges With Duplicates: 120
Total Edges: 353
Self-Loops: 44
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 40
Maximum Edges in a Connected Component: 352
Maximum Geodesic Distance (Diameter): 4
Average Geodesic Distance: 1.87133
Graph Density: 0.15304878
NodeXL Version: 1.0.1.179

Here is an example map of the connections among the people who tweeted the term “pawcon” in Twitter on September 14th, a week prior to the event.

Manu Sharma, Principle Research Scientist at LinkedIn gave a great presentation on the patterns found in their data.  Big data, for example, showed that most of the people who previously worked at recently failed banks and financial institutions have updated their profiles to show that they mostly have new jobs at some of the remaining companies in the industry.

The event was held at the New York Hilton: Maps & Directions

Threaded conversations are a major form of social media.  Message boards, email and email lists, twitter, blog comments, text messages, and discussion forums are all social media systems built around the message thread data structure.  As messages are exchanged through these systems, some messages are sent as a reply to a particular previous message.  As messages are sent in reply to prior messages, chains of messages form.  Message chains come in two major forms: branching and non-branching.  Branching threads are those that allow more than one message to reply to a prior message.  Non-branching threads are single chains, like a string of pearls, that allow only one message to reply to a prior message.  Many web based message boards are non-branching.  Many email systems and discussion forums are branching.

ThreadMill requires a minimal set of data elements to generate its reports.  A data table must minimally have a column of information for each message that includes the name of the message board, the forum, the thread, and the author, along with a unique identifier for each message and the date and time it was posted.  Optional data elements include the unique identifier of the message being replied to, the URL of the message, and the URL for a profile photo.

All forms of threaded message exchange can be measured.  Simple measures like the count of the number of messages or the number of authors are obvious and useful.  Other measures can be created from more sophisticated analysis.  For example, the network of connections that forms as different authors reply to one another can be extracted and analyzed using network analysis methods.  It is possible to calculate metrics from these networks of reply that describe the location of each person in the graph.

ThreadMill generates several data sets that can be used to create visualizations of the activity and structure of a message board collection.

A Treemap data set can illustrate the hierarchy of encapsulated authors within threads, threads within fora, fora within boards, and boards within collections.  Treemap visualizations of collections of threaded conversations can quickly highlight the most active or populous discussions.

An AuthorLine visualization takes the form of a double histogram, with bubbles representing each thread active in each time period sized by the volume of messages the author contributed, sorted by size.  Threads that have been initiated by the author are represented as bubbles above the center line.  Messages that the author contributes to threads started by other authors are represented as bubbles stacked below the center line.  AuthorLines quickly reveal the pattern of activity an author displays and identifies which of several types of contributors the author is.

A scatter plot visualization represents each author as a bubble in an X-Y space defined by the number of different days the author was active against the average number of messages the author contributes to the threads in which they participate.

A time series line chart reveals the days of maximum and minimum activity along with trends.

A network diagram reveals the overall structure of the discussion space and the people who occupy strategic locations within the network graph.

ThreadMill has received generous assistance from Morningside Analytics.  Bruce Woodson implemented ThreadMill.

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/

Here is the updated map of connections among people who recently tweeted #occupywallstreet:

These are the connections among the Twitter users who recently tweeted the word OccupyWallStreet 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: occupywallst.org/

A larger version of the image is here: www.flickr.com/photos/marc_smith/6232030871/sizes/l/in/ph…

Top most between users:
@ajenglish
@anonymousirc
@cnbc
@rawstory
@ryannewyork
@mediabistro
@blogdiva
@brentnhunter
@krenner2
@rebuilddream

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 3014
Edges With Duplicates: 720
Total Edges: 3734
Self-Loops: 994
Connected Components: 318
Single-Vertex Connected Components: 300
Maximum Vertices in a Connected Component: 664
Maximum Edges in a Connected Component: 3326
Maximum Geodesic Distance (Diameter): 9
Average Geodesic Distance: 3.508133
Graph Density: 0.002524525
NodeXL Version1.0.1.179

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

This year the IEEE Social Computing conference is being held in Boston, October 9-11, 2011.

The NodeXL team from the Social Media Research Foundation have a paper on our newest layout feature in NodeXL: Group-in-a-box.

Abstract: Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose Group-In-a-Box (GIB), a metalayout for clustered graphs that enables multi-faceted analysis of networks. It uses the treemap space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.

The paper is authored by:

Eduarda Mendes Rodrigues*, Natasa Milic-Frayling†, Marc Smith‡, Ben Shneiderman§, Derek Hansen¶
* Dept. of Informatics Engineering, Faculty of Engineering, University of Porto, Portugal – eduardamr @ acm.org
† Microsoft Research, Cambridge, UK -natasamf @ microsoft.com
‡ Connected Action Consulting Group, Belmont, California, USA – marc @ connectedaction.net
§ Dept. of Computer Science & Human-Computer Interaction Lab, University of Maryland, College Park, Maryland, USA – ben @ cs.umd.edu
¶ College of Information Studies, University of Maryland, College Park, Maryland – dlhansen @ umd.edu

A map of the connections among the people who recently tweeted #SocialCom2011:

Connections among the Twitter users who recently tweeted the word #socialcom2011 when queried on October 10, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

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/6232130442/sizes/l/in/ph…

Top most between users:
@danielequercia
@gadgetman4u
@bkeegan
@shaunlawson
@maryheston
@mmiiina
@ronaldomenezes
@theshadowhost
@fergal_reid
@cosleydr

Graph Metric: Value
Graph Type: Directed
Vertices: 36
Unique Edges: 119
Edges With Duplicates: 155
Total Edges: 274
Self-Loops: 105
Connected Components: 2
Single-Vertex Connected Components: 1
Maximum Vertices in a Connected Component: 35
Maximum Edges in a Connected Component: 273
Maximum Geodesic Distance (Diameter): 5
Average Geodesic Distance: 2.174551
Graph Density: 0.107936508
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.

I have enjoyed and been educated by my previous visits to South Africa and the Mobile Web in Africa conference, now in its 3rd year.

MWA gathers the many parts of the picture of mobile technologies in play across the African continent.

I will speak again at this year’s event, focusing on social media networks.  My partner is Walter Pike, a marketing professional with a specialty in digital media.

Here is the agenda for the workshop on Friday, November 25th.

Photos from the prior two years of Mobile Web Africa after the jump:

2010

2009

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.

Summer Social Webshop
on
Technology-Mediated Social Participation
University of Maryland, College Park
August 23-26, 2011

Eventful.  The 2011 Webshop at the University of Maryland was certainly that with both an earthquake and a hurricane to mark the start and end of the event.  We really moved heaven and earth at this workshop.

In 4  days, 20 talks, 45 students, an earthquake, a hurricane and many new connections – the Webshop touched on a set of related concepts, methods, and findings about ways to use communication and computation technology to help groups, neighborhoods, cities, states, and nations work collectively towards common goals.

Several years ago a program at the University of Maryland called “Webshop” (Web Workshop) was organized by Professor John Robinson and held for three consecutive Summers.  I visited and spoke at two of these events and know many people who attended or spoke at one or more and remember the event enthusiastically.   The students who attended include some of the now leading researchers in the field of social science studies of the internet.  There is an impressive alumni list.

The last Webshop was held in 2003 and many years and significant changes have occurred in the time since. Twitter, Facebook, StreetView, iPad,FourSquare, Android, Kinect, EC2, Mechanical Turk, Arduino, were all new or non-existent when the first Webshops were run.  Today we have more reason than ever to focus on the details and patterns of computer-mediated human association. Ever more people channel more of their communications with others through more digital media, often of the social kind.  A new data resource for the social sciences is growing in scale and promise: from billions of events it is possible to start to build a picture of an aggregate whole, and to start to grasp the terrain and landscape of social media.

After many years of inactivity, the Summer Social Webshop (@Webshop2011) happened again!  With the generous support of the National Science Foundation and additional assistance from Google Research, on August 23-26, 2011 at the University of Maryland, College Park, a group of students heard and engaged with more than two dozen leading researchers exploring digital social landscapes from a variety of perspectives.  Organized by a collaboration between the University of Maryland’s Human Computer Interaction Laboratory (HCIL), the College of Information Studies, the Sociology and Computer Science Department, and the Social Media Research Foundation, the event gathered students from a wide range of disciplines to get a concentrated dose of advanced efforts to gather data from social media and people’s understanding and practices around digital technologies.   Doctoral students in computer science, iSchools, sociology, communications, political science, anthropology, psychology, journalism, and related disciplines applied to attend the 4-day intensive workshop on Technology-Mediated Social Participation (TMSP).  The workshop explored the many ways social media can be applied to national priorities such as health, energy, education, disaster response, political participation, environmental protection, business innovation, or community safety.  The workshop attracted graduate students at US universities studying social-networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, recommendation systems, mobile and location aware media creation, and other social media.

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Organizers

Alan Neustadtl (@smilex3md) - Sociology, University of Maryland
Jennifer Preece (@jenpre) – iSchool, University of Maryland
Marc Smith(@Marc_Smith) - Social Media Research Foundation
Ben Shneiderman (@benbendc) - Computer Science, University of Maryland
PJ Rey (@pjrey) - Sociology, University of Maryland, Student Coordinator

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Photos