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):

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/2011/10/17/october-19-20-2011-nyc-predictive-analytics-world-network-maps-for-end-users-collect-analyze-visualize-and-communicate-network-insights-with-zero-coding/?utm_source=rss&utm_medium=rss&utm_campaign=october-19-20-2011-nyc-predictive-analytics-world-network-maps-for-end-users-collect-analyze-visualize-and-communicate-network-insights-with-zero-coding http://www.connectedaction.net/2011/10/17/october-19-20-2011-nyc-predictive-analytics-world-network-maps-for-end-users-collect-analyze-visualize-and-communicate-network-insights-with-zero-coding/#comments Mon, 17 Oct 2011 08:00:07 +0000 Marc Smith http://www.connectedaction.net/?p=4474  

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.

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

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.

Here is a map of the connections among the people who tweeted the term “AEJMC” on August 7, 2011:

The top most between people in this network are:@aejmc, @jlab, @karenrussell, @terryflynn, @natcomm, @tmccorkindale, @derigansilver, @tkell, @aejmconlineads, and @jeremyhl:

I will present a Workshop on Social Media Network Analysis and NodeXL at the 9 August 2011 – Association for Education in Journalism and Mass Communication (http://www.aejmc.com) in St. Louis, Missouri along with my colleague Professor Hernando Rojas, from the School of Journalism & Mass Communication, University of Wisconsin – Madison.

See: http://www.aejmc.com/home/events/annual-convention/

The Association for Education in Journalism and Mass Communication (AEJMC) is a nonprofit, educational association of journalism and mass communication educators, students and media professionals. The Association’s mission is to promote the highest possible standards for journalism and mass communication education, to cultivate the widest possible range of communication research, to encourage the implementation of a multi-cultural society in the classroom and curriculum, and to defend and maintain freedom of communication in an effort to achieve better professional practice and a better informed public.

Our session is:

Using NodeXL for Social Network Analysis
Tuesday — 2 pm to 5 pm
Presented by Communication Theory and Methodology Division
This pre-conference workshop examines social network analysis. Social network analysis can be used to examine message boards, blogs, and friend networks (amongmany other phenomena). Participants will learn to use the NodeXL program to conduct a network analysis. For information, contact Michel M. Haigh, Pennsylvania State University at mmh25@psu.edu.

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”:

A new NodeXL data provider (“spigot“) is now available to extract these social networks from Exchange servers.

This is the Exchange Spigot for NodeXL a configurable interface for defining and executing a query against a Microsoft Exchange email server designed to be used with NodeXL.

NodeXL is an easy to use add-in for the familiar Excel spreadsheet that adds support for network overview, discovery and exploration. The free and open NodeXL project has several import data providers that extract and display networks from popular social media network repositories like Twitter, Flickr, YouTube, the WWW and more.

By adding an Exchange Spigot to your installation of NodeXL you gain a simple way to extract networks from your own email stored on an Exchange server. With appropriate permissions and in compliance with local and legal requirements, the Exchange Spigot can access email and extract social network data across multiple email user accounts.

If available, Active Directory metadata is integrated with email data extracted from the Exchange server. Profile metadata is added to the network workbook when available (typically only for email from other employees, not people from outside your company or division).

The Exchange Spigot for NodeXL is one of several related projects to provide a connection between valuable sources of network data structures and the easy-to-use network browser, NodeXL. Additional projects have implemented connections to web crawlers, Facebook, and soon, SharePoint.

The Exchange Spigot for NodeXL project team includes: Luka Potkonjak, Milos Lazarevic, Ivan Vujic, Natasa Milic-Frayling, and Marc Smith.

The Exchange Spigot for NodeXL project is coordinated by the Social Media Research Foundation (http://www.smrfoundation.org) and receives generous support from Microsoft Research.

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.

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

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.

The Summer Social Webshop (@Webshop2011) is happening again!  With the generous support of the National Science Foundation and additional assistance from Google Research, this August 23-26 at the University of Maryland, College Park, a group of students will hear and engage 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 will gather 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 are invited to apply to attend this summer’s 4-day intensive workshop on Technology-Mediated Social Participation (TMSP).  The workshop explores 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 should be of interest to 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.

For more information, please contact Alan Neustadtl (alan.neustadtl@gmail.com).

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Photos

• 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.

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 NodeXL map of the connections among the people who tweeted the term PDA2011 OR PDA11:

The top most between people in this graph were: @anarchivist, @blefurgy, @naypinya, @rodanda, @jeffubois, @scottros, @evancaroll, @kevinmarks, @vurayav, and @sshreeves.

Here is the video from my talk:

I will be speaking at the next Predictive Analytics World in San Francisco– March 14-15, 2011 about measuring and mapping social media networks with NodeXL:

Social Data: Advanced Methods – Social (Media) Network Analysis with NodeXL
www.predictiveanalyticsworld.com/sanfrancisco/2011/agenda.php#day2-13a

Networks are the common data structure that unify the otherwise diverse range of social media services. In this session learn how to extract social networks from various social media systems and analyze and visualize the structures found in collections of connections. Learn to use the free and open NodeXL add-in for Excel 2007/2010 to analyze email, twitter, facebook, youtube, www, flickr, and wiki networks.

Here is an example – a map of the connections among the people who recently tweeted the string “#pawcon”.

Connections among the Twitter users who recently mentioned PAWCON when queried on March 14, 2011 including tweets from the period 3/10/2011 15:13 to 3/15/2011 3:51, scaled by numbers of followers.
NodeXL is available from www.codeplex.com/nodexl

The top most between contributors were: @tapan_patel, @jamet123, @zementis, @pawcon, @ibmspss, @sasanalytics, @kristinevick, @waynettetubbs, @dataspora, @inside_r

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)

Tell a Friend
Add to Calendar

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.

See: http://www.gapminder.org/videos/the-joy-of-stats/ for the recent BBC documentary featuring Professor Hans Rosling and his entertaining presentation of historical changes in world health and wealth statistics over two hundred years.

A related video is Journalism in the Age of Data by Geoff McGhee from the Digital Journalism Program at Stanford University.

Journalism in the Age of Data from Geoff McGhee on Vimeo.

I spoke at the Social Tech 2010 conference in San Jose on October 26, 2010.

The event focused on social media technologies and practices in the business to business space.

I spoke on a panel about the application of social network analysis to social media to illustrate the ways SNA can illuminate the shape and key contributors to a social media crowd inside the firewall or on the public Internet.

Other notable speakers included: Thomas Lento from Facebook, Lawrence Liu from Cisco and Michael Wu from Lithium.  The panel was moderated by Tatyana Kanzaveli.

An example of applying social network analysis and visualization is this map of the connections among the people who recently tweeted “socialtech” on the 25th of September, 2010.

The top most between users in this map can be sorted in the spreadsheet, bringing the user accounts for @mprofsevents, @socialtech, @mikewise07, @jowyang, @marketingprofs, @meganleap, @socialmediaclub, @cotweet, @zuberance, @spiral16.

CONNECTING THE DOTS
A Network Visualization Symposium

http://networks.iq.harvard.edu/

Date: October 22, 2010
The first CONNECTING THE DOTS symposium on network visualization organized by Michael Barnett, Jukka-Pekka Onnela, and Samuel Arbesman of The Christakis Lab at Harvard was held on October 22, 2010.

Keynote speakers:

Alessandro Vespignani, Professor of Informatics and Computing, Indiana University, Bloomington
Ben Fry, co-developer of Processing and data visualization expert

The NodeXL project will be represented by Ben Shneiderman who will speak at 2:00 PM on:

Analyzing Social Media Networks with NodeXL

Complete draft program for Connecting_the_Dots…

Program

10:15 AM Keynote I: Ben Fry (Fathom)

11:00 AM SocioPatterns
Wouter van Der Broeck, (ISI Foundation)

The SocioPatterns project developed an active-RFID based sociometric platform capable of sensing mutual proximity in a distributed, scalable fashion. This platform enables a large-scale, real-time assessment of the dynamics of person-to-person interaction network with a fine temporal and spatial granularity. The initial focus of this project was on empirical data collection in support of research on dynamic social interaction networks and infection dynamics. Various experiments also explored the potential of the platform for more service oriented applications, and artscience contexts. The SocioPattens platform has been deployed in more than a dozen settings, ranging from conferences and tradeshows, over primary schools, long-term public exhibitions, artscience projects, and domestic settings.

11:25 AM Visualizing Countrywide Traffic Dynamics
Wen Dong (MIT)

It is often a challenge to reveal the human mobility patterns in multi-gigabyte vehicle tracking data spanning a whole country and many years. We address this problem through a Markov model of how people drive in the road network, and animations of road-traffic conditions on top of Google Maps. We welcome researchers to use our visualization tool set.

12:50 PM Keynote II: Alessandro Vespignani (Indiana University)

1:35 PM Network Visualization Using Processing
Sergiy Nesterko (Harvard University)

Network, or relational data are becoming of increasing interest to researchers in different disciplines, partly because related problems are very hard to solve due to complex structure of underlying datasets. It is well known that a picture is worth a thousand words, and especially for networks related problems, visualization has become an invaluable tool in gaining intuition that helps tackle them. As an example, we consider network visualization using Processing as applied to problems in respondent-driven sampling, which is a variant of link tracing process to explore social networks of hard-to-reach populations. We emphasize the role of a compelling visualization in a successful research endeavor and briefly describe how Processing can be used to make it.

2:00 PM Analyzing Social Media Networks with NodeXL
Ben Shneiderman (Univ. of Maryland, College Park)

NodeXL is the free and open add-in for Excel 2007/2010 that supports network overview, discovery and exploration. Supported by Microsoft External Research for 3+ years, this effort has produced a game-changing software tool that enables students, researchers, and professional community managers to extract and download networks from email, Twitter, YouTube, Flickr, WWW, etc. Then they can compute network analytic metrics, filter out less relevant features, apply multiple layout algorithms, and produce compelling yet comprehensible displays that reveal actionable insights about complex social processes.

2:25 PM FacetAtlas: Multifaceted Visualization for Rich-context Networks
Yu-Ru Lin (Harvard University and Northeastern University)

FacetAtlas is a multifaceted visualization technique for analyzing interconnected text documents. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. The power of FacetAtlas is demonstrated through a case study that targets patient education in the health care domain.

3:35 PM Pathline: A Tool for Exploring Biological Data in the Context of Molecular Networks

Miriah Meyer (Harvard University)

Pathline is an interactive tool that visualizes the biologically interesting relationships between multiple molecular networks, the activity levels of multiple genes, and the evolutionary relatedness of multiple species. Working in close collaboration with a group of genomics scientists we designed Pathline around a representation of a linearized network that provides appropriate topological information and supports the comparison of quantitative data. We validated the efficacy of Pathline to both confirm existing knowledge and discover new scientific insights through a series of case studies.

4:00 PM Is Network Visualization Even Useful?
John Dila (Innocentive)

Our company, InnoCentive Inc. (innocentive.com) is an Open Innovation marketplace, which connects Seeker companies (that have tractable problems they have not been able to solve) with our Global Solver Community of experts, specialists, and generalists who sign up to solve tough, meaningful problems. As part of a website redesign, we wanted to update the way we graphically display data about our global Solver network. By “eating our own dog-food” we posted a challenge on our platform asking for solutions to how we can best visualize a selected data set about our global Solver population in order to drive traffic to our site and add value to customers and the business. We received 10 solutions and they are currently under evaluation. We will present our challenge process, the results of the visualization challenge, and how we plan to use them moving forward.

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

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

Yahoo! Speaker Series

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

2010 Speaker: Marc Smith
VIDEO: Marc Smith

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

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

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

I returned to South Africa to attend the 2010 edition of Mobile Web Africa, a conference focused on the remarkable adoption and development of mobile networks and technology in Africa.

The conference took place on 29th & 30th September 2010 in Johannesburg, South Africa.

I presented a workshop on social media network analysis, see: http://www.mobilewebafrica.com/marc-smith-workshop.php

The conference twitter hashtag #MWA2010 received a significant amount of traffic.  I mapped the collection of collections formed when people who tweeted “#MWA2010″ also followed, replied, or mentioned one another.  The following two EventMaps of the #MWA2010 twitter hashtag illustrates the development of the network.

Map of the connections among people who tweet “MWA2010″ on October 5th, 2010

Map of the connections among people who tweet “MWA2010″ on September 29th, 2010

The later graph is defined by three leading contributors who each generated significant retweeting along with mentions and replies.

I attended the previous 2009 Mobile Web Africa conference in Johannesburg, South Africa, organized by AllAmber Media which also featured a focus on the next wave of mobile devices and services.

Mobile is BIG in Africa.  The “Remote control of the Universe” = Mobile Phone.

The speaker from Samsung last year noted that for many Africans, the mobile device is their first and main method for managing digital objects.  A phone is not just a phone, it is a still and video camera, music player, watch, web browser, flashlight, wallet and file system.

The representative from Yonder Mobile Media noted that mobile phones have now exceeded the installed base of the previously most widely used communication technology: FM Radio.  But mobile is different from FM: mobile devices are two way, provide a method for exchanging payments, and are a primary method of access to the internet for many billions of people.  Mobile is the “first” screen for many people in Africa, not the third screen role that it plays in more developed economies.  Regional services like MXIT and The Grid are offering Internet services tailored to affordable mobile devices.

Photos from this year’s event:

Photos from last year’s event:

Photos from Cape Town:

2010 - Sept - UC Davis CTSA SNA Community Engagement

CTSA Community Engagement and Social Network Analysis Workshop
September 22-24, 2010
University of California Davis
Clinical and Translational Science Center
Sacramento, California

OBJECTIVES

• Understand the basic concepts, tools, and techniques of Social Network Analysis (SNA) and how they relate to the evaluation of CTSAs and their impact on community research networks.
• Have sufficient exposure to a basic software tool for inputting and analyzing network data in order to undertake a simple social network analysis.
• Enumerate some possible uses of SNA and how to develop data collection tools or use existing sources to study the process and impact of CTSA on community engaged research.
• List primary authors of SNA analyses relevant to CTSA and community engaged research.

Presentation by Noshir Contractor from Northwestern University

10:30-Noon Demonstration of NodeXL Software

Marc Smith.

NodeXL is a free and open add-in for Excel that supports network overview, discovery and exploration. A contributor to NodeXL will provide basic instruction in its use, how to generate useful network statistics and metrics, how to create visualizations of network graphs and how to filter and display attributes that can be used to highlight important structures in networks.

1:00-1:45 Using NodeXL

Joe Hunt, MPH, Indiana University (with Marc Smith)

Using NodeXL to visualize interactions between investigators and CTSA key functions.

The Second Workshop on Information in Networks
September 24-25, 2010, New York City

Sponsored in part by the Initiative on Information in Networks
Organizers: Sinan Aral, Foster Provost, Arun Sundararajan

The second Workshop on Information in Networks (WIN10) will be held this year September 24-25, 2010, again in New York City. From the program description:

“Last year’s workshop brought together a small yet influential community around topics that at their core involve ‘information in networks‘—its distribution, its diffusion, its value, and its influence on social and economic outcomes. Scholars from fields as diverse as computer science, economics, information systems, marketing, physics, political science and sociology came together to lay the foundation for ongoing relationships and to build a multidisciplinary research community. This year’s workshop will build on this foundation toward bringing more innovative content and vibrant discussion to the forum. Speakers will share their recent research, which may have been published elsewhere, but which may not be widely known outside of their own disciplines. The workshop will combine invited and contributed talks with poster presentations selected from a pool of submitted abstracts. We hope the energy of New York City will inspire the gathering, and that our participants will leave with new ideas and a renewed sense of community.”

Ben Shneiderman and Jenny Preece will speak about their work on social media applied to national priorities with a talk titled: “Promoting National Initiatives for Technology-Mediated Social Participation“.  The talk includes their work creating NSF workshops on Technology-Mediated Social Participation (www.tmsp.umd.edu), the paper Reader-to-Leader Framework: Motivating technology-mediated social participation (which appeared in the AIS Transactions on Human-Computer Interaction in March 2009), and recent work with the Encyclopedia of Life (www.eol.org), and  NodeXL projects.  Here is the abstract.

WIN10 speakers include:
==============================
Ron Burt, University of Chicago
Nicholas Christakis, Harvard University
Nathan Eagle, MIT
Sanjeev Goyal, Cambridge University
Matthew Jackson, Stanford University
Jenny Preece, University of Maryland
Ben Shneiderman, University of Maryland
Tony Jebara, Columbia University
David Jensen, University of Massachussetts
Michael Kearns, University of Pennsylvania
Rachel Kranton, Duke University
David Lazer, Northeastern University
Mark Newman, University of Michigan (tentative)
Alex Sandy Pentland, MIT
Alessandro Vespignani, Indiana University
Stanley Wasserman, Indiana University
Duncan Watts, Yahoo! Research

The book