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

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

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

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

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

Venues and Dates

Johannesburg

30 November 2011
Gordon Institute
of Business Science,
Illovo

Pictures:

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

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

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

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

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

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

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

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

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

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

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

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

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

CCSR Short Course
Social Media Network Analysis using NodeXL

November 9th  9.00 am – 5.30 pm.

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

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

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

Course Objectives
After this course, participants will:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

My slides: 20111103 con tech2011-marc smith

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

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

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

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

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

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

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

These are the connections among the Twitter users who recently tweeted the word #pawcon when queried on October 19, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

A larger version of the image is here: www.flickr.com/photos/marc_smith/6261006732/sizes/l/in/ph…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

 

Please vote on our SxSW 2012 panel:

Thanks for your vote for our SxSW panel!

The ACM CHI 2011 Conference is taking place in Vancouver, BC.

May 10, 2011

May 8, 2011

These are the connections among the Twitter users who recently tweeted the word chi2011 when queried on May 8, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.

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

A larger version of the network map is here: www.flickr.com/photos/marc_smith/5701022497/sizes/o/in/ph…

Top most between users:
@chi2011
@msftresearch
@edchi
@jeffbigham
@landay
@evanmpeck
@acagamic
@clifflampe
@yardi
@eytanadar

Graph Metric: Value
Graph Type: Directed
Vertices: 421
Unique Edges: 3849
Edges With Duplicates: 324
Total Edges: 4173
Self-Loops: 0
Connected Components: 32
Single-Vertex Connected Components: 29
Maximum Vertices in a Connected Component: 384
Maximum Edges in a Connected Component: 4146
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.553249
Graph Density: 0.022587943

NodeXL Version1.0.1.167

Data set in NodeXL format.

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.

• 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!

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

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

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

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

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

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

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

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

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

I will be speaking at the Oxford Internet Institute on July 13 and 14, 2011 as part of the Summer Doctoral Program.

I will speak about the use of social network analysis and visualization techniques to analyze and understand social media.

The Summer Doctoral Program is designed to

…bring together advanced doctoral students engaged in dissertation research relating to the Internet and other ICTs. By sharing their work and learning from leading academics in the field, students can enhance the quality and significance of their thesis research and create a peer network of excellent young researchers.

The 2011 Summer Doctoral Programme will draw substantially upon the OII’s research strengths and will involve participation from many of our faculty, together with colleagues from other partner institutions. It will emphasise methodological innovation and good practice in research design, and will expose students to the benefits of discussing their research in a multi-disciplinary teaching environment.”

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

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

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

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

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

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

Web Science Doctoral Summer School 2011

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

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

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

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

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

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Today, we release a new NodeXL feature in version  .164  that we think will improve the results generated by other layout algorithms: Group Layout.  In the Layout Options menu in the NodeXL Network Graph Pane (called “Document Actions” by Excel) you will find that the dialog box has been updated:

Selecting the new “Lay out each of the graph’s groups in its own box and sort the boxes by group size” option and checking the “Show each box’s outline” can generate an image that neatly places each group in a bounded region.

Here we see the set of connections among the people who tweeted the string “msrtf11 OR techfest” – referring to the Microsoft Research TechFest 2011 event that took place in early March in Redmond, Washington.  This map clarifies the set of set of relationships among sub-groups within the graph.

Contrast this with the layout created without this feature turned on:

We still have some refinements coming that will improve this layout further (for example, the neat grid of isolated nodes takes an extra step today, we will try to make that happen automatically soon) but this is a nice step in making network graphs easier to understand.  Let us know what you think!

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

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

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

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

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

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

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

On 11 March 2011, I will be speaking at:

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

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

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

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

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

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Institute for Communication Technology Management

http://www.marshall.usc.edu/faculty/centers/ctm/events

On March 10th, 2011 I will speak on a panel at the USC Marshall School of Business Institute for Communication Technology Management Executive Roundtable.

]]> http://www.connectedaction.net/2011/03/05/10-march-2011-usc-marshall-school-of-business-communication-technology-executive-roundtable/feed/ 0 http://www.connectedaction.net/2011/01/20/workshop-january-28th-2011-salinas-ca-mapping-social-media-networks-with-nodexl/?utm_source=rss&utm_medium=rss&utm_campaign=workshop-january-28th-2011-salinas-ca-mapping-social-media-networks-with-nodexl http://www.connectedaction.net/2011/01/20/workshop-january-28th-2011-salinas-ca-mapping-social-media-networks-with-nodexl/#comments Fri, 21 Jan 2011 07:00:41 +0000 Marc Smith http://www.connectedaction.net/?p=3975

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

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

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

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

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

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

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

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

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

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

A continental breakfast will be provided.

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

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

Agenda

Morning session:

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

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

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

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

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

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

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

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

Afternoon session:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NodeXL Version 1.0.1.159

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

Previous HICSS papers:

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

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

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

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

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

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

Here are some sample event graphs:

I have a MacBookPro.  I can run Windows very nicely using various Virtual Machine products like VMWare’s Fusion or the free and open VirtualBox.  In these virtual machines I can run Windows and Office and then NodeXL.  It’s pretty neat to see Windows inside the Mac OS window.  I am always amazed that it works at all.

But I try to avoid doing this as the main way I run NodeXL at almost any cost.  My Mac session slows to a crawl, my Windows session is slow, and the overall usability of the system degrades too much.  If you focus on JUST NodeXL in the VM it works well, but context switching is too demanding.  So I mostly focus on NodeXL on a Windows machine.  When I travel I usually just have my Mac laptop and miss having a zippy version of NodeXL at hand.

Recently, I discovered that Amazon EC2 offers a remarkable way to have my cake and eat it too: I created a modestly powered .micro instance of Windows XP and Office 2010 and installed NodeXL.  I then use the Microsoft Remote Desktop Client for Mac OS X to get a window into the remote virtual instance.  This system can be a bit slow, it responds a bit like a NetBook, but it does not slow down my Mac OS X instance and can be reached from any machine with the Remote Desktop client and internet access.  For those who need a more speedy response or to handle larger data sets, Amazon is happy to sell more powerful instances of Windows for modestly more pennies per hour.  For example, the .small instance is merely $0.13/hour or $3.12/day and offers much faster responses. Of course, if you turn the instance off when you are not using it, Amazon does not charge anything.

Recently Adam Fields, my colleague at Morningside-Analytics did me the great favor of recording the process of creating a new .micro instance of an Amazon EC2 Windows system.

aws_instance_creation
Following each step will lead you to the creation of your own virtual machine into which you can install Office and NodeXL.  You can then access this image, which runs continuously until you Terminate it – even when you close computer you use to remote desktop.  This is both a bug and a feature – the system runs no matter the state of the computer you use to access it.  Just remember to really turn off the instance if you do not want to pay a recurring fee! You have to actually _Terminate_ the instance to avoid paying fees shutting it down isn’t enough.

Also note that the AMI Adam used in the video was a 64-bit one, which doesn’t have a “small” option, it jumps straight from micro to large. If you want the small option, you have to use the 32-bit AMI, which is right above the one he picked.

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.

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

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

Yahoo! Speaker Series

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

2010 Speaker: Marc Smith
VIDEO: Marc Smith

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

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

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

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

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

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

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

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

Have a look and see what patterns you can find.