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 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:

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/2011/10/16/threadmill-0-1-social-accounting-for-message-thread-collections/?utm_source=rss&utm_medium=rss&utm_campaign=threadmill-0-1-social-accounting-for-message-thread-collections http://www.connectedaction.net/2011/10/16/threadmill-0-1-social-accounting-for-message-thread-collections/#comments Sun, 16 Oct 2011 13:00:44 +0000 Marc Smith http://www.connectedaction.net/?p=4387 The Social Media Research Foundation is pleased to announce the immediate availability of ThreadMill 0.1.  ThreadMill is a free and open application that consumes message thread data and produces reports about each author, thread, forum, and board along with visualizations of the patterns of connection and activity.  ThreadMill is written in Ruby, and depends on MongoDB, SinatraRB, HAML, and Flash to collect, analyze, and report data about collections of conversation threads.

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.

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/

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/

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/

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

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.

-

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

-

Photos

I will speak at Purdue University on September 22 and 23, 2011 about mapping social media networks.

My host is Sorin Matei, professor of Communications, who has been researching the social structure of social media networks.

I will also speak at Professor Matei’s class: COM 63200 On-line Interaction and Facilitation

Here is an example map of the connections among the people who tweeted the word “Purdue” on September 16th, 2011:

Connections among the Twitter users who recently tweeted the word Purdue when queried on September 16, 2011, scaled by numbers of tweets (with outliers thresholded). Connections created when users reply, mention or follow one another.

See: www.purdue.edu/

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

(Edges connecting users are bundled and curved with recent features added to NodeXL v.177.)

A larger version of the image is here: www.flickr.com/photos/marc_smith/6155750905/sizes/o/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:
@lifeatpurdue
@jajuanjohnson
@stfu_gabby
@purdueexponent
@charliienosheen
@mbrister2
@boilerfootball
@purduesports
@hipandresanbo
@cheesebrrrrr

Graph Metric: Value
Graph Type: Directed
Vertices: 1000
Unique Edges: 4045
Edges With Duplicates: 706
Total Edges: 4751
Self-Loops: 977
Connected Components: 429
Single-Vertex Connected Components: 395
Maximum Vertices in a Connected Component: 528
Maximum Edges in a Connected Component: 4134
Maximum Geodesic Distance (Diameter): 12
Average Geodesic Distance: 3.517707
Graph Density: 0.003507508
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.

2011 ASA meetings are being held in Las Vega, Nevada.  Several sessions are related to the study of the social uses and effects of information technology are hosted by the Communications and Information Technology Section of the American Sociological Association (CITASA).

These are the connections among the people who recently tweeted the term “ASA2011″ on 18 August 2011.

Several papers and panels related to the sociology of the internet will take place:

Saturday, Aug 20 – 2:30pm – 4:10pm

 124. Section on Sociology of Law Paper Session. Privacy in the Digital Age: Law, Culture, and Contention I (co-sponsored with the Section on Collective Behavior and Social Movements and Section on Communication and Information Technology)

Monday, Aug 22 – 8:30-9:30AM Roundtables

338. Section on Communication and Information Technology Roundtable Session

Monday, Aug 22 – 9:30-10:10AM Business and Awards Ceremony

(immediately follows roundtables)

Monday, Aug 22 – 10:30AM – 12:10PM 

376. Section on Communication and Information Technology Invited Session. Social Media in Community Action and Social Change

Monday, Aug 22 – 2:30pm – 4:10pm

419. Section on Communication and Information Technology Paper Session. New Media Frontiers: Youth and New Media

Monday, Aug 22 – 5:00-7:00PM, Section Reception, hotel suite, Caesars Palace

Location to be announced at all CITASA sessions and meetings.

Tuesday has the main day of panels and talks:

Tueday, August 23 – 10:30am – 12:10pm

514. Section on Sociology of Culture Invited Session. Privacy in the Digital Age II: Law, Culture, and Contention (co-sponsored with the Section on Collective Behavior and Social Movements and Section on Communication and Information Technology)

Regular Session. Social Participant On and Off the Internet.

Session Organizer: Barry Wellman (University of Toronto)
Sunday 1030-12

The Active Few and the Sporadic Many: Making Sense of Opinion Dynamics in Online Review Systems
*Fedor Dokshin (Cornell University)

The Internet, Young Adults and Political Engagement around the 2008 Presidential Elections

*Aaron Shaw (University of California-Berkeley), Eszter Hargittai (Northwestern University)

Social movements’ use of information and communication technologies: diffusion, usefulness and empowerment

*Piotr Konieczny (University of Pittsburgh)

*=presenter

———————————–

Regular Session. Networked Individuals On and Off the Internet

Scheduled Time: Sun, Aug 21 – 2:30pm – 4:10pm

Session Participants:

Session Organizer: Barry Wellman (University of Toronto)
Presider: Barry Wellman (University of Toronto)

“If you’re not texting me, you must be old:” Age Cohort Attachment to Mobile Phones
*Amanda M. Warr (University of Alabama at Birmingham), Shelia R. Cotten (University of Alabama at Birmingham), William A. Anderson (University of Alabama at Birmingham)

The Intersection of Gender and Place in Online Health Activities
*Melinda Goldner (Union College), Timothy M. Hale (University of Alabama at Birmingham), Shelia R. Cotten (University of Alabama at Birmingham), Michael J. Stern (College of Charleston), Patricia Drentea (University of Alabama-Birmingham)

Core Discussion Networks Over Time: How the Internet Reduces Social Isolation and Increases Network Diversity
*Keith N. Hampton (University of Pennsylvania), Lauren Sessions Goulet (University of Pennsylvania)

Social Network Sites, Social Ties, and Social Capital
*Randy Lynn (George Mason University), *James C. Witte (George Mason University)

Discussant: Barry Wellman (University of Toronto)

———————————————————-

Regular Session. Digital Divides in the Internet Age
Scheduled Time: Mon, Aug 22 – 4:30pm – 6:10pm

Session Organizer: Barry Wellman (University of Toronto)
Presider: Barry Wellman (University of Toronto)
Cyberasociality and the Online Sociality Divide: Third Level Digital Divide?

*Zeynep Tufekci (University of Maryland-Baltimore County)
(Tech) Culture in Action: Using a cultural framework to describe and explain differences in technological competency

*Cassidy Puckett (Northwestern University)
Does the “Do-it-yourself approach” reduce digital inequality? Lack of digital skills and infrequent Internet use

*Uwe Matzat (Eindhoven University of Technology), Bert M. Sadowski (Eindhoven University of Technology)
Stalled Digital Divide: Gender Differences in the Use of ICT in Taiwan

*Yu-Hua Chen (National Taiwan University), Wen-Yin Chien (United Daily News)

 

Please vote on our SxSW 2012 panel:

Thanks for your vote for our SxSW panel!

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.

On June 22nd in New York City Wired Magazine and the Economist hosted a day of discussions and displays related to the theme of “Nextwork” hosted by Juniper Networks and located at the Tribeca Rooftop.

The Social Media Research Foundation displayed and printed  Nextwork Networks maps of the connections among the people who tweeted about the conference, the speakers, and their companies.

Visit the NextWork Network booth hosted by the Social Media Research Foundation to see a map of the connections among people who tweeted the term “NextWork“.  Networks are important data structures but the tools to access, analyze and visualize networks have been hard to use.  New tools now make networks as easy to handle as making a pie chart.  NodeXL, the free and open network overview, discovery and exploration add-in for the familiar Excel spreadsheet, makes creating network diagrams and gaining insights much simpler (see: http://nodexl.codeplex.com).  Using NodeXL, we have been collecting all the tweets that mention the NextWork event and building a network map of the ways those people connect to one another.  Not everyone is equally connected: some people occupy more strategic locations in the web of connections than others.   Find out who: request your own NodeXL social media network map for your Twitter account or the name of your company or brand (or your competitors) by dropping by the booth (or visit: http://www.smrfoundation.org/nextwork2011).

Here is an example, a map of the connections among the people who recently tweeted the name “Kevin Kelly”, one of the speakers at the event:

This is a map of the connections among the people who tweeted the term “Nextwork”:

Bits exist along a gradient from private to public.  But in practice they only move in one direction.

Thus, there are two destinies for information: public or oblivion.

Information wants to be copied.

This is not the same as information wanting to be free (or expensive), or information wanting *you* to be free.  Information probably prefers to be free because it may increase the rate at which it is copied, not because it is inherently liberating to the user.  In fact, the “free” quality of some information is probably not liberating at all.  Copying and liberty are orthogonal.

Information diffuses over time: access rights to information can expand over time, but only rarely (ever?) does data become less available, and once available publicly, information is almost never entirely private again.

With enough copies on enough devices, information becomes essentially public. The state of being public may come in degrees, some things are more public than others.  Much information is public in principle but enjoys security by obscurity. Obscurity is eroded by increasing availability of computing resources that make collection and machine analysis affordable at large scales.  The banality of data is no protection.  “No one cares what I think/do/say/click” is not a valid assumption.  In aggregate the banal is data and fuel to many business models.  Maybe no one *cares* what you tweet, click, buy or search for, but many businesses make it their business to aggregate these scattered faint signals and build detailed profiles to drive commerce and customized views of data.

Some information is destroyed, never to be recovered.  This is the only way information can avoid eventually (potentially) becoming public. But less and less data now meets this fate.  Delete is a declining feature of many systems.

Information that is not public and has not yet been destroyed is just waiting to change to either state.

Despite security systems, many private bits are eventually exposed by people passing material to someone else who then accidentally makes them public, or they do so unintentionally themselves by leaving files in publicly accessible locations that are visited by search engine spiders and other web crawlers.  Even professionally managed private data repositories are subject to subsequent distribution, infiltration or error. Data spills are becoming more common. Billions of records are hemorrhaged  into the public regularly.  If well funded organizations cannot secure their information, the rest of us should take note.

It may not be possible for big organizations or any organization to secure their networks, or even do so sufficiently effectively to give users a practical period of privacy, however short.  Eventually private bits, even when encrypted (no matter how well), become public because the march of computing power makes their encryption increasingly trivial to break and their exchange over networks (no mater how well secured) is subject to leaking, intentional and otherwise.  Private bits may only have a “half-life” during which they retain their non-public existence.  The length of this half-life may itself be getting shorter.   Mary Branscome suggests that there could be a physical law in operation: the natural entropy of access control lists?

All bits that persist are destined to be public, and once public never to be private again. Unless they are destroyed.

I argue that the only bits that you cannot find are the ones you need right now. The only bits you cannot get rid of are the ones that are most embarrassing to you right now.  Just because you cannot find the bits you want does not mean that no one else can find those bits.

All your bits are belong to us.

This issue is getting more important as we are invited to use systems that promise selective sharing of data and other tools generate ever more data to potentially share.  Anything that puts your bits into the cloud promises selective sharing.  I believe and hope my much beloved Dropbox account is separate from all the others, except for the one’s I chose to share with. And I think it is, expect for that glitch they had, the details of which elude me (but I think we’re good now, and I so depend on Dropbox I do not know what I would do without it). But all these walls are just made out of a few lines of business logic and an Access Control List. ACLs rule our access to digital objects with an iron fist until they don’t for the many human and technical reasons mentioned.  Like most human infrastructures these selective sharing mechanisms are subject to failure and attack.

Now new sources of data captured from the details of everyday life by sensors and  services are increasingly recorded by external systems and by people themselves, generating new streams of archival material that is richer than all but the most obsessively observed biographies.

Many organizations are adopting social media and creating data sets that can map their internal social network structure as an accidental by-product of their communication practices.  Studying these data sets is a focus of growing interest.  Research projects like SenseCam are now becoming products and existing services like MingleSticks, Poken, FourSquare, and Google Latitude already deliver many of these features. Devices like iPhone and Android phones are weaving location information into every application.

Some steps are still in progress: when my phone notices your phone a new set of mobile social software applications become possible as whole populations capture data about other people as they beacon their identities to one another. Additional sensors will collect ever more medical data with the intent of improving our health and safety, as early adopters in the “Quantified Self” movement make clear.

But the  consequences of data diffusion are becoming difficult to predict.  Social media systems are being linked to one another to enable cascades of events to be triggered from a single message as status updates are passed among Facebook, LinkedIn, Twitter, and blogs.  Tools now automatically aggregate the results of searches and post articles that themselves may trigger other events.  Taking a photo or updating a status message can now set off a series of unpredictable events.

Add potential improvements in audio and facial recognition and a new world of continuous observation and publication emerges.  Some benefits, like those displayed by the Google Flu tracking system, illustrate the potential for insight from aggregated sensor data.  More exploitative applications are also likely.

The result will be lives that are more publicly displayed than ever before.  The collapse of roles (“lowest common denominator culture”) described by Bernie Hogan (listen starting in about 40 minutes – but the entire talk is good and worth a listen) as described by the sociologist Erving Goffman may be one consequence: we are interacting with everyone when we interact with anyone.  Secret shared meanings may still be possible — but selectively shared bits are not, at least not very reliably so in the short term and almost certainly not in the medium term.

Therefore, all services that promote the idea of “selective sharing” are selling a myth.  The more you trust that information you generate can be contained, the more potential there is for an “explosive decompression” as data intended for an individual or a small group becomes suddenly available to a large group or a complete population. Private bits are in a state of high potential energy, always poised to become public.

Engineering is the science, art and practice of containing and directing  forces. Information system engineers might be up to the challenge of delivering selective sharing.  And when combined with law, regulation and social practices, technology could make selective sharing real the way that engineers manage the flow of powerful but dangerous flows of high pressure steam through power plants.  However, recently even high pressure steam engineers working with nuclear fuels have faced some very bad failure conditions beyond their predicted scope.  Information technologists may face analogous issues when managing high pressure containers of selectively shared information.

My policy is not to give up all forms of privacy, I still keep my email and other data behind passwords that I do not (knowingly) share.  I share lots of pictures on flickr but not all of them are public.  I would prefer to keep lots of financial, medical, and personal stuff selectively shared.  I’d like these features to work.

But I have started to understand that my data is likely to be open to others, if not now then some day — and probably sooner than I expect. The net/cloud  holds a good sized and growing  chunk of my digital life and I would like selective sharing features (if I could handle the cognitive tax of managing them).  I just do not believe it is a reasonable expectation.  In a world of increasing interconnection and unifying name and search spaces, data may not be something you can keep local for long.

Tools that suggest that we can reliably segregate content and limit its diffusion are suggesting that water does not roll down hill.  Those who believe that are likely to get wet.

I am very interested in the Quantified Self conference to be held in Mountain View, California, May 28 and 29.  While I have attended just a few of the in-person meet-ups, which were engaging and intriguing events, I have followed the blog and tweet stream closely.  These events feature short presentations about practices, prototypes, and products that record information about our own behavior and activity.  It is great to hear that, as the meet-ups grew to become very large, a conference has been organized to accommodate the demand and growing interest in the intersection of sensors and other devices with medical and personal self-monitoring.  Using a variety of devices, our lives can now create detailed inscriptions that illuminate our behavior and patterns with novel clarity and detail. (See: http://quantifiedself.com/conference/)

I plan to attend.  I will speak on Sunday morning at a 10:30 breakout session on using social media network analysis to map personal and collective social media spaces.

Quantifying the “Quantified Self” discussion in Twitter: Here is a map of the connections among the people who recently tweeted the string “quantified self” on May 25th, 2011.

Click for larger image.

The top most between participants in this graph were:

@timoreilly
@quantifiedself (-1)
@harscoat (-)
@edyson
@qsparis
@egadenne (-4)
@agaricus (-)
@brunoaziza
@adriana872
@chloester

Bolded users also appeared in a previous map made earlier in the year, on January 20, 2011, which looked like this:

This is the list of the most “between” users in this network on January 20th, 2011:

The most between participants in this graph are: @quantifiedself, @egadenne, @harscoat, @genomera, @neufit, @jxa, @agaricus, @emergentorder, @bulletproofexec, @2healthguru.

The topics discussed in the quantifiedself tweet stream can be rendered as a network graph based on words that co-occur:

• Group Frames: If your graph has groups and you choose to lay out the groups in their own boxes (NodeXL, Graph, Layout, Layout Options), you can now specify the width of the box outlines.

• Constant Edges: When you select an edge, its width no longer changes. NodeXL used to use the same width for all selected edges, even if the edges had varying widths when unselected.
• Group and Vertex Display Harmony:
  • When a graph has groups, you now have more control over how the groups are shown. Go to NodeXL, Analysis, Groups, Group Options.
  • The NodeXL, Show/Hide, Graph Elements, Groups menu item has been replaced with a checkbox in the Group Options dialog box.

  

• Right-Click Group Controls: Menu items for selecting, expanding, collapsing and removing groups are now available in the menu that appears when you right-click the graph pane. (These are just shortcuts for the same menu items that are available in the Ribbon at NodeXL, Analysis, Groups.)

• WYSIWYCC: What You See Is What You Can Click -
  • Hidden edges and vertices (those that have their Visibility cells set to Hide) can no longer be selected in the graph pane.
  • Edges and vertices that have been filtered (NodeXL, Analysis, Dynamic Filters) can no longer be selected in the graph pane.
• Bigger Twitter Lists: When importing a Twitter list network (NodeXL, Import, From Twitter List Network), you can now enter up to 10,000 usernames. The maximum used to be 500.
• UCINET / Matrix Compatibility: Bug fix: When exporting the graph to a UCINET file (NodeXL, Data, Export, To UCINET Full Matrix DL File), isolated vertices didn’t get exported. When exporting the graph to a new matrix workbook (NodeXL, Data, Export, To New Matrix Workbook), isolated vertices didn’t get exported, when importing a graph from a matrix workbook (NodeXL, Data, Import, From Open Matrix Workbook), isolated vertices didn’t get imported. Now they do!

29 June – 2 July 2011
Queensland University of Technology

Tweet with the hashtag #ct2011

http://ct2011.urbaninformatics.net/

C&T 2011 OVERVIEW

The biennial Communities and Technologies (C&T) conference is the premier international forum for stimulating scholarly debate and disseminating research on the complex connections between communities – both physical and virtual – and information and communication technologies.

C&T 2011 welcomes participation from researchers, designers, educators, industry, and students from the many disciplines and perspectives bearing on the interaction between community and technology, including architecture, arts, business, design, economics, education, engineering, ergonomics, information technology, geography, health, humanities, law, media and communication studies, and social sciences. The conference program will include competitively selected, peer-reviewed papers, as well as pre-conference workshops, a doctoral consortium, and invited keynote and panel speakers.

Marcus Foth
Conference Chair

IMPORTANT DATES
30 April 2011: Workshop papers, Doctoral Consortium and Student Volunteer applications due
29 June 2011: Pre-conference workshops and Doctoral Consortium
30 June – 2 July 2011: C&T 2011 conference at QUT, Brisbane, Australia

CONFERENCE TOPICS

C&T 2011 welcomes contributions in all areas of community and technology research, design and development. In addition, we particularly invite authors to address any of the following topics:

Augmented Reality
Civic Intelligence
Context and Location Awareness
Community-centred Design and Evaluation Methodologies
Community Engagement
E-research with Communities
E-government and E-governance
Participation
Smart Community Services
Sustainability
Universal Usability and Accessibility
Urban Informatics
Tangible Interfaces for Community Interaction
Technologies of Scale Making
Visualisation Techniques
Working across Cultures

http://ct2011.urbaninformatics.net/

#ct2011

I will be speaking at the Jacob T. Schwartz International School for Scientific Research week long Lipari School on Computational Social Science , July 17 – July 23, 2011, Lipari Island, Italy.

This year’s program is dedicated to Computational Social Science: Text and Decisions

Speakers:

• Claudio Cioffi-Revilla: Director of the Center for Social Complexity, Krasnow Institute for Advanced Study, George Mason University, Washington DC.
• Huan Liu: Community Detection and Mining in Social Media [abstract]
  School of Computing, Informatics, and Decision Systems Engineering, Arizona State University
• Roel Popping: Computer-assisted text analysis, and the relevance of decision making and text mining [abstract]
  Department of Sociology, University of Groningen

Tutorials

• Marc A. Smith: Charting Collections of Connections in Social Media: Maps and Measures with NodeXL [abstract]
  Chief Social Scientist, Connected Action Consulting Group
• Calogero Zarba: Introduction to matrix algebra [abstract]
  Neodata Intelligence s.r.l., Italy
• Alessandro Pluchino: Netlogo: An agent based simulation programmable environment [abstract], University of Catania, Italy

http://www.ischools.org/iConference11/2011index/

Our paper, Finding Social Roles in Wikipedia,  about the variety of  roles people perform in Wikis received the best paper award (along with 4 others) in a field of 86 papers.   The 2011 iConference accepted 86 papers, and had about 550 attendees.

The paper is authored by: Howard T. Welser at Ohio University, Austin Lin at Cornell University and Microsoft, Dan Cosley, Fedor Dokshin, Gueorgi Kossinets and Geri Gay at  Cornell University, and Marc Smith from Connected Action.

The paper pdf pre-print is available here: http://oak.cats.ohiou.edu/~welser/Welser.Cosley.plus.Wiki.Roles.pdf

The link to the ACM abstract and pdf: http://portal.acm.org/citation.cfm?id=1940778&CFID=9933318&CFTOKEN=58981138

Abstract: This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential “structural signatures” that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month’s cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.

Web Science Doctoral Summer School 2011

I will be attending and speaking at the upcoming 2011 Web Science Trust Graduate Summer School tobe held at DERI, NUI Galway Ireland from July 6th to July 13th, 2011.  I will speak about social media network analysis and run a workshop on using NodeXL.  There is a packed schedule featuring a great group of researchers who are gathering at the event, including:

* Prof. Wendy Hall is Professor of Computer Science at the University of Southampton, UK, and Dean of the Faculty of Physical and Applied Sciences.
* Prof. Nigel Shadbolt is Professor of Artificial Intelligence (AI) and Deputy Head (Research) of the School of Electronics and Computer Science at the University of Southampton.
* Prof. Stefan Decker is a professor at the National University of Ireland, Galway, and director of the Digital Enterprise Research Institute.
* Dr. Harith Alani is a senior lecturer at the Knowledge Media Institute, where he is heading a group specialising in Social Semantics.
* Dr. Bernie Hogan is a Research Fellow at the Oxford Internet Institute. He specializes in novel methods for online data capture and analysis, especially via social media.
* Prof. Scott Kirkpatrick has a background in physics (AB Princeton, PhD Harvard) and 15 years of developing new technologies at IBM’s TJ Watson Research Center before coming to the Hebrew University in 2000.
* Prof. Enric Plaza holds a Ph.D. in Computer Science by the Technical University of Catalonia (UPC) and is Research Professor of the Spanish Council for Scientific Research (CSIC).
* Prof. Steffen Staab is the director of Institute WeST – Web Science and Technologies and of the Institute for Computer Science of the University of Koblenz-Landau, Germany.
* Dr. Derek Greene is a Research Fellow at the School of Computer Science and Informatics, University College Dublin. As part of the Clique Research Cluster, his focus is on network analysis and community finding, with a particular emphasis on dynamic mobile and social media networks.
* Dr. Michael Hausenblas is a Research Fellow at DERI, NUI Galway, where he leads the Linked Data Research Centre.
* Dr. Daniele Quercia is Horizon researcher at the University of Cambridge (UK). he is interested in computational social science, web science, and social computing.
* Dr. Markus Strohmaier is an Assistant Professor at the Knowledge Management Institute, Faculty of Computer Science at Graz University of Technology.

The event is focused on the methods and theories needed to grasp the nature of the social implications of the rapid changes in the technologies of communication and computation:

“The Web is the largest technological artefact in existence, comprising a global network of information sites and services. It is a social machine, delivering information between people and communities, embedded in almost all processes of human society: education, medicine, science and technology, commerce, entertainment and social activity. It is often simply supposed that the Web is a neutral technology, a stable computing platform for the delivery of information and services. What is overlooked is that the Web is changing constantly in response to the demands of human society. Incremental innovations leads to changes in how people use the Web and in turn how Web technology responds to changed human interaction. Small technological decisions influence how individuals use the Web and ripple out to have unanticipated macro-effects. Sometimes these effects are beneficial such as the emergence of Web 2.0 technologies. Other technologies such as the rise of spam bots or ‘blackhat’ search engine optimisation techniques clutter the Web with irrelevant, distracting information. While influential corporations such as Google, Facebook and Microsoft generate huge revenues from the Web, the Web itself is owned by everyone and no-one. We need to fully understand the demands placed on the Web by human society, so that its fate does not lead to a ‘tragedy of the commons’ but to a sustainable technological resource for the future.”

http://webscience.deri.ie/index.html

Read More

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.

On 11 March 2011, I will be speaking at:

Organizational and Social Media Network Analysis:
Next Practices in Organizational Networks, Analytics and Media

Along with Valdis Krebs, Chief Scientist, http://thenetworkthinkers.com/

Valdis will be speaking about Organizational Network Analysis - Introduction to Enterprise Networks, followed by a InFlow 3.1 Workshop: Social Network Analysis with InFlow 3.1

I will be speaking about Social Media Network Analysis with NodeXL.

Preservation Park
1233 Preservation Park Way
Oakland, CA 94612
(510) 874-7580
(click for map)

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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.

Mapping Social Networks with NodeXL:
Finding Direction in a Sea of Connection

The Community Foundation for Monterey County has a Social Network Support project that has already applied network analysis techniques to map networks of nonprofit organizations (http://www.cfmco.org/index.cfm/id/18/Social-Network-Support/). They recognize that awareness and improved methods of working in networks can provide opportunities for new ideas, increase information flow, and help systems change. Mapping networks is one method to assess the structure of communications and work patterns. As a result, the foundation is hosting a workshop on Friday, January 28th, that will focus on improving network effectiveness by creating maps with NodeXL, the free, open-source network mapping tool. The Community Foundation for Monterey County is generously opening the introductory workshop free to the community! The workshop is free but pre-registration is required (http://cfmcmapping.eventbrite.com).  If you do plan to attend, I encourage you to download NodeXL prior to the workshop and bring a Windows laptop as well as any data sets you already have and want to map during the workshop. NodeXL is compatible with Microsoft Office 2007 and 2010 and Windows XP, Vista, and 7. NodeXL is not compatible with Microsoft Office 2003 or Office 2008 for Mac.  It is possible to rent a low cost virtual Windows machines from Amazon and access them on a Mac using remote desktop (see: How to run NodeXL on a connected Mac (or other platform) using Amazon EC2). Of course, you may want to get the companion book: Analyzing social media networks with NodeXL: Insights from a connected world prior to the workshop as well.

If you cannot attend I believe there will be a video recording that will be made available shortly after the event.

This workshop is a hands-on guide to extracting and analyzing a social network graph (with NO programming). Using examples from Twitter, YouTube, Email, flickr, and participant submissions, we will review the steps needed to take a network graph from collection through analysis and visualization to insight. Learn to tell stories about key people and groups with network visualizations. Participants will receive an introduction to social network analysis and NodeXL; be able to import network data; create a network map; and generate an actionable insight from a network map.

NodeXL allows users to generate network maps by importing data from Microsoft Office Excel. Participants are encouraged to download NodeXL prior to the workshop and bring a laptop and data sets when they attend to use during the workshop.

NodeXL is available from: http://nodexl.codeplex.com/ and is a project managed by the Social Media Research Foundation with generous support from the Microsoft Research External Research Projects Group.

Friday, January 28, 2011
9:00 AM to 5:00 PM
Hartnell College
411 Central Ave
CALL 208 (Center for Assessment and Lifelong Learning)
Salinas, CA

An agenda is below and full workshop description is available at:

http://www.cfmco.org/index.cfm/id/111/Workshops-Professional-Development/

The workshop is free; however space is limited to 60.

A continental breakfast will be provided.

Participants are responsible for purchasing $1.00 Hartnell College parking permit.

The CALL Building is near the parking structure on Central Avenue near Villa Street.
http://www.hartnell.edu/about/hartnell_campus.html

Agenda

Morning session:

Introduction to social network analysis, examples and use of social network maps, intro to NodeXL.

Introduction to the history of social network analysis – how to “think link“.

Core vocabulary: node/vertex, link/tie/relationship, degree, centrality, core/periphery, bridge, hole, density.

Thinking by seeing.- Visualizing networks: pictures of collections of connections.

Networks are everywhere: biological, commercial, social, technical. How to extract the networks around you: how to build your own “edge list”.

Living in a sea of tweets, links, likes, views, reads, ratings, reviews, comments, connections, tags, ties, edits, plays, check-ins, contacts, friends, follows, favorites, Networks are newly self-documenting: machine readable connections now comprise the majority of our connections with one another, making our social worlds more available to analysis than ever before.

Introduction to NodeXL: mechanics of getting a graph open, analyzed and visualized.

Hands-on: how to analyze a social graph. Using examples from Twitter, YouTube, Email, flickr, and participant submissions, we take a graph from collection through analysis and visualization. Telling stories about key people and groups with network visualizations.

Afternoon session:

Extracting networks that matter to your organization: defining the nature of the nodes and edges in a graph is the fundamental question in network analysis. Who are the people, organizations, processes, entities that matter to you and how are they tied, connected, or linked? How to query your existing logs and data sets to extract useful network graphs.

Decorating network graphs: using size, color, transparency, visibility, images, and location to better tell a story with a network graph.

Filtering a network: revealing key structures and nodes. Use dynamic filters to remove edges and nodes from the graph to reveal only those who meet specific criteria. Use a range of attributes to limit the graph to remove clutter and occlusion.

Scheduling regular data collection with NodeXL. A guide to using the free and open data collector to schedule the creation of network data sets on a regular automatic basis.

Future directions for NodeXL: time series and graph comparison, additional data sources, web interfaces.

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: