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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

My slides: 20111103 con tech2011-marc smith

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

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

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

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

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

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

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

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

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Organizers

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

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Photos

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.

A detailed outline of all the topics covered is available:

The help file will be updated as new features are added.

NodeXL Excel Template Help File:

An Overview of Network Graphs
A Quick Tour of the NodeXL Workbook
The NodeXL Worksheets
The Graph Pane
The NodeXL Ribbon Tab
Creating a Simple Graph
Directed vs. Undirected Graphs
Graphs with Isolated Vertices
Zooming, Moving Around and Scaling the Graph
Zooming the Graph
Moving Around the Graph
Scaling the Graph
Selecting Graph Elements
Selecting Vertices
Selecting Edges
Changing How the Graph Looks
Default Visual Properties: Graph Options
Setting Visual Properties for Individual Edges, Vertices or Groups
Automatically Calculating Visual Properties for All Edges, Vertices or Groups
Labeling Edges and Vertices
Changing How the Graph is Laid Out
Layout Algorithms
Selectively Laying Out Parts of the Graph Again
Snapping Vertices to a Grid
Layout Options
Saving an Image of the Graph
Analyzing the Graph
Calculating Graph Metrics
Creating Subgraph Images
Using Dynamic Filters
Working with Options
Using the Current Workbook’s Options for New Workbooks
Exporting and Importing Options
Resetting All Options
Working with Groups
Creating Groups
Creating Groups by Vertex Attribute
Creating Groups by Connected Component
Creating Groups by Cluster
Manually Creating Groups
Understanding the Group Worksheets
How Groups Are Shown in the Graph Pane
Selecting Groups
Collapsing and Expanding Groups
Removing Groups
Showing and Hiding Workbook Columns
Showing and Hiding Graph Elements
Importing Graph Data
Importing Graph Data from Other Programs
Importing Graph Data from Another Workbook
Importing Graph Data from Email
Importing Graph Data from Online Social Networks
Exporting Graph Data
Exporting Graph Data to Other Programs
Exporting Graph Data to Another Workbook
Merging Duplicate Edges
Automating Common Tasks
The NodeXL Network Server
Where to Go for More Information

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.

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

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:

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

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

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

Link on the BBC

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

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

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

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

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

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

On November 8th, 2010 I spoke in Seoul, Korea at the 3rd International Conference on Digital Culture on the topic of “Smart Society and Civic Culture“.

Several of my colleagues also spoke at the event, including Leslie Tkach-Kawasaki from the University of Tsukuba, Japan, Han Woo Park from YeoungNam University, Korea, George Barnett from the University of California, Davis and Robert Ackland from The Australian National University, AU.

Robert Ackland from The Australian National University, AU

George Barnett from the University of California, Davis

Han Woo Park from YeoungNam University, Korea

Leslie Tkach-Kawasaki from the University of Tsukuba, Japan

Marc Smith, Connected Action Consulting Group

Han Woo Park‘s research team from YeoungNam University, Korea

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:

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:
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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

Mapping mobile social networks with NodeXL: finding key users, groups, and locations

The video is now available:

The video reviews the creation of maps like this:

That illustrate the connections among people who tweet the term “#ecomm2010″, scaled by the number of followers.

Abstract: Social network analysis (SNA) is a powerful method for gaining insight into the massive collections of connections created when many people connect to one another through mobile devices. SNA has been widely applied to desktop social media and is moving into the mobile world. Prominent studies of the “call graph” have been produced at national scales.

Mobile providers are applying SNA to identify key subscribers who can reduce churn and help gain adoption of new services and products. Network analysis has historically had a steep learning curve, but now new tools are making SNA easier for less technical users. This talk will describe social network concepts and their application to mobile data sets. A free and open add-in for the popular Excel 2007 spreadsheet called NodeXL (http://www.codeplex.com/nodexl) can perform many complex SNA tasks like data import, scrubbing, metrics calculation, clustering, and visualization. Applying this tool to call graph and subscriber data sets can reveal key positions in the network that can attract and hold other subscribers in the system.

Examples of network analysis of social media and mobile data sets can be found on the Connected Action blog (http://www.connectedaction.net).

The 2010 Microsoft Research Faculty Summit was held July 12 and 13 in Redmond, Washington.  Among the many panels and discussions related to the state of computer science the NodeXL team had several representatives talking about the ways network science education can be expanded using an easy to use application for network analysis built on Excel.

Jimmy Lin from the University of Maryland also attended to speak about programming in the cloud.

Here is the abstract for the NodeXL talk:

NodeXL – Social Network Analysis in Excel—Natasa Milic Frayling, Microsoft Research; Ben Shneiderman, University of Maryland; Marc Smith, Connected Action

Businesses, entrepreneurs, individuals, and government agencies alike are looking to social network analysis (SNA) tools for insight into trends, connections, and fluctuations in social media. Microsoft’s NodeXL is a free, open-source SNA plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data. But it goes further than other SNA tools—NodeXL was developed by a multidisciplinary team of experts that bring together information studies, computer science, sociology, human-computer interaction, and over 20 years of visual analytic theory and information visualization into a simple tool anyone can use. This makes NodeXL of interest not only to end-users but also to researchers and students studying visual and network analytics and their application in the real world. NodeXL has the unique feature that it imports networks from Outlook email, Twitter, flickr, YouTube, WWW, and other sources, plus it offers a rich set of metrics, layouts, and clustering algorithms. This talk will describe NodeXL and our efforts to start the Social Media Research Foundation.

Some photos from the event:

Saul Greenberg

Ben Shneiderman and Andy van Dam

Ben Shneiderman, Natasa Milic-Frayling and Marc Smith

Tom McMail and Marc Smith

Pierre has been a deep student of telecommunications policy regulation in the United States for many years.  He has generated a remarkable network map built from the details of filings to the FCC over more than a decade.  These filings are made by companies when they agree or disagree with a proposed policy.  When two companies file in support (or opposition) to the same policy they create a tie between them.  The collection of these connections creates a complex network of coalitions and factions.

http://www.cmu.edu/joss/content/issues/2010jossviz/5_deVries.htm

“The graph is derived from meta-data associated with documents that are filed electronically whenever an organization interacts with the FCC, in accordance with the Administrative Procedures Act. Whenever a letter, comment or other document is filed, the filer provides information on the parties involved, number of pages, relevant proceedings, date, etc.”

…

“Once the data is cleaned up, an edge list is created in Excel by running another VBA macro. A graph is created from this list with NodeXL, a social network analysis and visualization add-in for Excel 2007. NodeXL’s Fruchterman-Reingold algorithm is used to prepare a preliminary layout; nodes are then moved by hand into visually intelligible positions, respecting the clusters suggested by NodeXL’s implementation of the Wakita-Tsurumi algorithm. Nodes are colored on the basis of eigenvector centrality. The degree of investment that organizations make in lobbying is measured by the total number of filings it made in this proceeding over the period of study, and reflected in the size of the node. This information is obtained by running another VBA macro against the underlying ECFS metadata, and then matching that to the vertices in the graph.”

Read more about this industry network at JoSS.

Ben Shneiderman: Methods and Tools for Facilitating Social Participation from User Experience Israel (UXI) on Vimeo.

Nice review of information visualization techniques and some mention of recent work on the NodeXL project.

Fourth International AAAI Conference on Weblogs and Social Media
(ICWSM-10)
May 23-26, 2010
George Washington University, Washington, DC

Sponsored by the Association for the Advancement of Artificial Intelligence

The ICWSM 2010 conference starts Sunday.  This is a very high quality conference on the study of social media.  My colleague, Professor Derek Hansen, and I will lead a tutorial on using NodeXL to analyze social media networks.

SA2: Introduction to Social Media Network Analysis
Marc Smith (Connected Action) and
Derek Hansen (University of Maryland)

Social networks are the defining data structure of social media, created as people reply, link, click, favorite, friend, re-tweet, co-edit, mention, or tag one another. In this tutorial, we review the core concepts and methods of social network analysis and apply it to the collection, analysis, and visualization of social media networks. Using the free and open NodeXL application, learn how to extract a social media network and generate metrics and visualizations that highlight key people and positions within streams of tweets, videos, photos, or emails.

AuthorLine visualizations represent the weekly rates and nature of contribution from a single author over the span of a year.  Each vertical strip of bubbles represents the activity of that author in a week.  The 52 strips of bubbles in these images represent a year of contribution history.  If users reply to threads other users initiate, they get blue bubbles that  sit below a middle dividing line.  Each thread to which they contribute in that week is represented as a separate bubble.  Each additional message contributed to that thread in that week adds to the size and transparency of the bubble.  If the author initiates new threads, they get a red bubble that sits above the line.

The results is an “at-a-glance” view of the pattern of participation for a year in the life of a threaded conversation message contributor.  The two images below illustrate just how different contributors can be in their patterns of engagement in a threaded conversation environment.  This image represents a contributor of mostly reply messages who starts a small amount of thread initiation at the end of the year but who contributes significantly more to threads started by others.  They have a habit of engaging in lengthy discussions, adding dozens of messages to one or more threads almost every week of the year.

http://www.boardtracker.com/cgi-bin/display_member.pl?board=4202&member=524613

Contrast this image with the distinct pattern created by this author who heavily initiates new threads while responding to those started by others more modestly until the pattern inverts at the ned of the year where they shift to a more heavy reply rather than initiation pattern.

http://premium.boardtracker.com/cgi-bin/display_member.pl?board=4202&member=501671

The paper that described the early work on these visualizations can be found in the HICSS 2004 conference:

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]

A related paper applies these visualizations to document the range of variation found in social media spaces built around threaded discussions.

Tammara Turner, Marc Smith, Danyel Fisher and Howard Ted Welser, Picturing Usenet: Mapping computer-mediated collective action. Journal of Computer Mediated Communication, 2005. [Local copy]

[Thanks to BoardTracker CEO Ron Kass for these images!]

Along with my colleague Eduarda Mendes-Rodriguez I will be speaking at the University of Porto next Friday, March 26th.

Hands-on Workshop – Social Media Network Analysis in NodeXL

This workshop provides an overview of Social Network Analysis (SNA) and its application to social media. The network or directed graph is a common structure in a wide range of different kinds of social media. Social Network Analysis is a set of tools, concepts, and techniques that can help measure a graph and the location and connection pattern of each component part.

Using NodeXL, workshop participants will learn how to take data from common social media sources (including enterprise discussions and online communities, Twitter, Flickr, your own email) and perform various types of analysis. Through this workshop, participants will:

· be able to understand the basics of SNA, its terminology and background.
· be able to transform communication data (e.g. Twitter, email, Flickr, message boards etc.) into network data.
· understand the different possible presentations of social networks, e.g. in a matrix or a sociogram.
· apply network metrics and visualizations to find clusters and key contributors in real world social media data.
· get familiar with the use of standard SNA tools and software in general and the NodeXL social network analysis add-in for Excel in particular.
· be able to derive practical and useful information through SNA analysis that would help design an innovative and successful online community.

Saving Our Present for the Future: Personal Archiving 2010

From fa