If you are attending the CHI 2010 conference in Atlanta and are interested in social media network analysis, consider attending this tutorial:

CN03: Introduction to Social Network Analysis
Time: Monday, 12 April 2010, 11:30 to 18:00
Organizers: Marc A. Smith, Panayiotis Zaphiris, C.S. Ang, Derek Hansen
Benefits
This course provides an overview of Social Network Analysis (SNA) and demonstrates through theory and practical case studies how it can be used in HCI (especially computer-mediated communication and CSCW) research and practise. This topic is of particular importance due to the popularity of social networking websites (e.g. YouTube, Facebook, MySpace etc.) and social computing. As people increasingly use online communities for social interaction, new methods are needed to study these phenomena. SNA is a valuable contribution to HCI research as it gives an opportunity to rigorously study the complex patterns of online communication.

Social network theory views a network as a group of actors who are connected by a set of relationships. Actors are often people, but can also be nations, organizations, objects etc. Social Network Analysis (SNA) focuses on patterns of relations between these actors. It seeks to describe networks of relations as fully as possible. This includes teasing out the prominent patterns in such networks, tracing the flow of information through them, and discovering what effects these relations and networks have on people and organizations. It can therefore be used to study network patterns of organizations, ideas, and people that are connected via various means in an online environment.

Audience
We welcome practitioners and academics interested in computer-mediated communication, universal design, and social software. No background knowledge about Social Network Analysis or statistics is required.
Origins
Versions of this course were delivered as tutorials at HCII 2007, 2009, Nordi-CHI 2009, INTERACT 2009, the INSNA Sunbelt Social Network Analysis Conference, the 2009 Communities and Technologies Conference and is scheduled to be run this Summer at the Stanford Media – X workshop and the 2009 Social Computing Conference . The content of the course is also part of the Inclusive Design course at the Cyprus University of Technology and has been taught in the context of a class on Communities of Practice at the University of Maryland, College of Information Studies. The course is constantly being revised and modified to account for current trends in this dynamic and emerging research area.
Features
Upon completion of this course, participants will:

• understand the basics of SNA, its terminology and background;
• be able to transform communication data (e.g. YouTube, MySpace etc.) to network data;
• understand the different possible presentations of social networks, e.g in a matrix or a sociogram;
• know practically how SNA can be applied to HCI (especially CMC) analysis;
• 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; and
• be able to derive practical and useful information through SNA analysis that would help designers cultivate more successful online communities.

Instructors

• Dr. Marc A. Smith is a sociologist and Chief Social Scientist at Connected Action Consulting Group, a provider of fine quality social media analysis platforms and systems. Smith specializes in the social organization of online communities and computer mediated interaction. He founded and managed the Community Technologies Group at Microsoft Research in Redmond, Washington and is now leading the development of social media reporting and analysis tools for Connected Action. Smith lives and works in Silicon Valley, California. [ Marc@connectedaction.net, http://www.connectedaction.net, http://delicious.com/marc_smith/]
• Dr. Panayiotis Zaphiris is the departmental coordinator and an Associate Professor at the Department of Multimedia and Graphic Arts of the Cyprus University of Technology. His research interests lie in HCI with an emphasis on inclusive design and social aspects of computing. More information can be found at http://www.zaphiris.org .
• Dr. C.S. Ang is a Lecturer at Kent University, UK. His research interests include social aspects of computer games, computer-mediated communication and agent-based simulation of social networks.
• Dr. Derek Hansen is assistant professor at the University of Maryland, College of Information Studies and the Director of the Center for the Advanced Study of Communities and Information. Hansen focuses his research and teaching on social computing, mass collaboration, consumer health informatics, and information services.

I will attend and speak at a symposium being heldMarch 24-25, 2010 at the IE University Department of Communication in Segovia, Spain.  The topic is: Transnational connections: Challenges and opportunities for communication.

“The Symposium aims to generate discussion on cutting-edge ideas in political communication, encourage international cooperation and unite scholars and practitioners.”

The symposium is organized with Center for Global Communication Studies at the Annenberg School for Communication, University of Pennsylvania.

Organizer and founding Dean of IE School of Communication, Samuel Martín-Barbero notes that the event will gather:

“More than forty international panelists, moderators and speakers (who) will not only reflect on the state of the field, but will also discuss cutting-edge advances in theory, research and practice.”

I will attend along with my colleague John Kelly, from the Berkman Center for Internet & Society, Harvard University and Founder of Morningside Analytics.

Here is the Program, a link to the Registration, and the Twitter stream for the conference.

I will speak about “Analyzing Internet social media: visualizing the social networks in computer networks”, details follow:

Social media systems on the Internet are sociologically interesting: why do some online groups succeed where others fail?  How do different collections of online media and populations of authors differ from one another?  How do patterns of contribution vary and how do these differences illustrate the roles people play within their communities?  Several visualizations of patterns of contribution and connection in a range of Internet social media including web boards, enterprise social networks services, and personal email are presented to illustrate the range of variation among social media repositories and between types of contributors.  These images suggest that a more comprehensive overview of social media can generate sociologically relevant findings, improve community management tasks as well as provide features that can improve search and ranking of user generated content.  A freely available tool, NodeXL, will be demonstrated to perform basic social media analysis tasks.  Extending these tools to include mobile social software (“mososo”) data sets is a major new direction.   In the not too distant future, mobile devices will possess a range of sensors and become  more “socially aware”.  When phones routinely notice each other the nature of social interaction will change dramatically.  How will places and  locations change when machines become socially aware?  In this talk, sociologist Marc Smith, Chief Social Scientist for Connected Action Consulting Group, a provider of social media analysis platforms and services, will describe these new technologies and some ways of thinking about their implications.

The Annual Meeting of the Israel Internet Association (http://www.isoc.org.il (English)) was held February 22-23 2010. I spoke at this year’s meeting: http://www.isoc.org.il/conf2010/agenda.php?lang=en

Part 1

Part 2

The previous year’s conference website is at: http://www.isoc.org.il/conf2009/program.php

The Israel Internet Association is the official Israeli Chapter of the Internet Society.  Their annual meeting is a central event of academics (sociologists, psychologists, business and law) as well as industry participants from sectors including mobile cellular companies and internet service suppliers.

After speaking at the Israel Internet Association conference February 22, I will speak at IBM Research in Haifa at an event co-sponsored by the University of Haifa Department of Sociology and Anthropology and the M.A. program in Sociology of Technology.  My hosts are Professor Gustavo Mesch from the university and Dr. Adam Perer from IBM research.

Wednesday, February 24^th, 1 pm

IBM Building
Haifa University

Title: Visualizing collections of social media connections: using social network analysis to assess, evaluate and measure social media engagement

Abstract: Social networks are created whenever people interact.  These networks become more visible when interactions take place through social media.  Social networks form when people link, reply, comment, edit, tag, and friend one another.  Sub-populations are formed whenever people mention the same company, products, event, topic, or personality.  Using social network analysis on collections of social media connections reveals important patterns: how are people clustered and grouped, where are the gaps, who plays the roles of bridge, hub, and isolate? In this talk I will display maps of twitter, you tube, flickr, and enterprise email systems and demonstrate several tools that can be used to collect, analyze, map and monitor social media, including the free and open NodeXL (network overview, discovery and exploration) add-in Excel 2007.

Here, for example, is a map of the connections among people who recently mentioned “haifa” in twitter sized by number of followers:

Here are some photos taken during the trip:

Saving Our Present for the Future: Personal Archiving 2010

From family photographs and personal papers to health and financial information, vital personal records are becoming digital. At the same time, creation and capture of new digital information has become a part of the daily routine for hundreds of millions of people. But what are the long term prospects for this data?

The combination of new capture devices (more than 1 billion camera phones will be sold in 2010) with the move from older forms of media is reshaping both our personal and collective memories. The size and complexity of personal collections growing, these collections are spread across different media (including film and paper!), and the lines between personal and professional, published and unpublished are being redrawn.

Whether these issues are described as personal archiving, lifestreams, personal digital heritage, preserving digital lives, scrapbooking, or managing intellectual estates, they present major challenges for both individuals and institutions: data loss is a nearly universal experience, whether it is due to hardware failure, obsolescence, user error, lack of institutional support, or any one of many other reasons. Some of these losses may not matter; but the early work  of the Nobel prize winners of the 2030s is likely to be digital today, and therefore at risk in ways that previous scientific and literary creations were not. And it isn’t just Nobel winners that matter: the lives of all of us will be preserved in ways not previously possible.

On Tuesday, February 16, the Internet Archive will host a small conference for practitioners in personal digital archiving.

The morning sessions will be devoted to examples of current practice; the afternoon discussion will focus on developing recommendations for institutions and individuals, and on developing a research agenda. Among the questions we would like to discuss:

- What new social norms around preservation, access, and disclosure are emerging?
- How can we cope with the shift from simple (e.g. text) to rich media (e.g. moving images) in personal collections?
- What is the gap between current possibilities for preserving personal collections, and what is actually needed by both individuals and institutions?
- What tools and services are needed to better enable self-archiving?
- What new economic models to support personal archives may be evolve?
- What are the long term rights management issues? Are there unrecognized stakeholders we should begin to account for now?
- Can we better anticipate (and measure) losses of personal material?
- Do libraries, museums, and archives have a new responsibility to collect personal materials?
- What has already failed? Can we generalize about approaches that are likely to fail over time?
- What are the options for cultural heritage institutions — libraries, museums, and archives — that want to preserve the personal collections of citizens and scholars, creators and actors?
- What might be the risks of building archiving systems that are “too good” or overly applied?

For individuals, institutions, investors, entrepreneurs, and funding agencies thinking about how best to address these issues, this meeting will include a variety of examples that may be replicated, and will sharpen the questions (technical, social, economic) around personal archiving.

For further information, please contact Jeff Ubois (jeff@ubois.com).

Time for another NodeXL update: sparklines!  Sparklines are a nifty and compact way of displaying a line chart in a small area.

Setting dynamic filters in NodeXL has been somewhat like rummaging around in the dark: without a way to see the distribution beneath a filter the user only knows the max and min values, not where the bulk of the observed data is located.  This version of NodeXL (1.0.1.111) features an improvement to the Dynamic Filters feature used to limit the nodes and edges displayed in the network visualization pane.  Earlier versions of Dynamic Filters allowed users to select a range for each attribute associated with the Edges and Vertices worksheet.  In the last version of NodeXLwe added an automated feature for creating those distribution histograms and placing them in a stack on the Overall Metrics worksheet after the user runs the “Graph Metrics” feature.  That is helpful but the worksheet is far from the user when they are setting the ranges within the Dynamic Filters dialog.  Now, the current release adds “sparklines” to the Dynamic Filters dialog box: as you set the upper and lower bounds for any network edge or vertex attribute, you can see how much of the distribution is included and excluded in the display.

This is one of several features added to NodeXL to make it easier for users to explore their networks and find actionable insights.  We have added sparklines in the dynamic filters interface in NodeXL so that you can now see the shape of a value’s distribution as you set the maximum and minimum values to be included in the filter.  Histograms also now appear in the Overall Metrics tab of the NodeXL worksheet where they convey the distribution of the major network attributes in the graph.

Table Of Contents

Foreword
Introduction
About the Question
Related Concepts
Choosing Subjects: Where Does Responsibility Matter Now?

Genetics And Healthcare
Thomas Murray, The Hastings Center
Ignacio Chapela: Drawing a Boundary Around the Lab
Arthur Caplan: Innovation as Politics
David Magnus & Mildred Cho: True Fictions

Nanotechnology
Christine Peterson: Nanotechnology and Enhancement
Lawrence Gasman: Nanomarkets

Robotics And Computing
Ronald Arkin: Embedding Values in Machines
Jeff Jonas: Applying the UN declaration of human rights
Marc Smith: Invention, mitigation, accounting and externalities
Mikko Ahonen: Open Innovation … and Radiation Safety

Design
Roberto Verganti: Varieties of Design Innovation
Michael Twidale: IRBs, Design, Empowerment,
Accountability, Sustainability

My comments from the volume are after the fold…

Marc Smith: Invention, Mitigation, Accounting and externalities

If responsibility is about effects, then systems of measurement and observation are key to any understanding of responsible innovation.

Dr. Marc Smith (http://www.connectedaction.net) is formerly a Senior Research Sociologist leading the Community Technologies Group at Microsoft Research in Redmond, Washington. His group focused on computer-mediated collective action, and studied and designed enhancements for social cyberspaces.  Smith now leads the Connected Action Consulting Group in Silicon Valley. In particular, he is interested in the emergence of social organizations like communities in online conversation and annotation environments. The goal is to identify the resources groups need in order to cooperate productively.

Smith is the co-editor of Communities in Cyberspace, which explores identity, social order and control, community structures, dynamics, and collective action in cyberspace, and has developed software called Netscan that measures and maps social spaces in the Internet, starting with the Usenet. A related effort, Project AURA, allows users to associate conversations (and more) with physical objects using mobile wireless devices and web services.

Responsibility in innovation often comes after the fact. “The history of the technology is seize new power—then mitigate, mitigate, mitigate all the pathologies,” Smith says. “How long was it from the Model T Ford to safety belts?”

Responsibility can be improved through measurement of effects. “May I suggest that your best method for mitigation is documentation of negative externalities?” Smith says. “You can ‘govern’ innovation when the language and the data to document negative consequences are more available, freely available, easily used. Then you have a technology regulation and negative externality problem, and that I think is one that is more tractable.”

Part of that is stakeholder identification. Smith argues that it is easy to identify stakeholders, at least after the fact, because, “They’re at the top of the legal documents that are served to you. That’s how you identify the stakeholders, the ones that actually get the job done, make themselves known, tell you that you’re creating negative externalities for them, and insist that you [provide] remedy.”

But it’s also important to recognize the initial position of most innovators. “Innovation typically comes from the people who are most squeezed out of the sweet solution space,” Smith says. “You don’t innovate unless you have to… Innovation is the behavior, I think, of marginal actors in an ecological landscape.”

We will add axis markings and titles soon, making these charts ready to use in a variety of network reports.  These histograms will also appear in the Dynamic Filters dialog to guide users as they select segments of the distribution to include or filter out of the displayed network graph.

Other updates:

1.0.1.110 (2010-02-03)

• The Overall Metrics worksheet now includes more information about the degree, in-degree, out-degree, betweenness centrality, closeness centrality, eigenvector centrality, and clustering coefficient metrics when those metrics are computed. The additional information includes the minimum, maximum, average, and median metric values, and a histogram showing the metric value distribution.
• The “Convert Old Workbook” item on the NodeXL, Data, Import menu in the Ribbon is now called “Import from NodeXL Workbook Created on Another Computer.” This menu item can be used to work around the following problem: NodeXL workbooks created on a 64-bit Windows computer cannot be opened directly in Excel on a 32-bit Windows computer, and vice-versa. (If you attempt to do so, you will get an error message whose details include “could not find a part of the path.”)
• A Clear All Worksheet Columns Now button has been added to the Autofill Columns dialog box (NodeXL, Visual Properties, Autofill). Also, you can now clear an individual worksheet column by clicking a button in the dialog box’s Options column.
• Bug fix: On large-font machines, the buttons at the bottom of the Autofill Columns dialog box didn’t fit within the dialog box.
• Bug fix: In some circumstances, vertices were drawn below the bottom of the graph pane and were impossible to see. One such circumstance was when the selection was exported to a new workbook (NodeXL, Data, Export, Selection to New NodeXL Workbook). The graph pane in the new workbook acted as if it were taller than its real height, leading to vertices dropping off the bottom.

Along with Professors Ben Shneiderman (Computer Science/Human Computer Interaction Lab) and Derek Hansen (College of Information Studies) from the University of Maryland I am writing and editing a book about analyzing the social media networks that form whenever people link or reply to one another, favorite, rate, read, or edit data about other people or their objects.  Social media networks can be analyzed using the methods of social network analysis, the mathematical application of graph and network theory to the social sciences.  Using social network analysis collections of connections can be analyzed and compared to identify key people and groups and measure changes over time and following interventions.

I am pleased to announce that we have signed with Elsevier/ Morgan Kaufmann to produce a book: Analyzing Social Media Networks with NodeXL: Insights from a Connected World for a Summer 2010 delivery!

A map of the relationships among the population of people who all tweet a particular keyword can lead to the discovery of the key hubs and influential people in the network.  A social network analysis of reply patterns in email collections displays clusters around projects and highlights key people and relationships.  Visualizing the connections among your friends in Facebook can reveal the various life stages and communities in which you have participated.  When you chart the links between videos and users in YouTube content with interesting network properties is exposed based on well connected content creators and influential commentators.  A graph of  the individual connections between flickr users illustrates the emergent formation of groups around social networks, locations, and topics.

These kinds of social media network data collection, scrubbing, analysis, and display tasks have historically required a remarkable collection of tools and skills.  A great example of the variety of tools that can be used in concert to extract, analyze and display social media networks can be found on Drew Conway’s blog.  This is a powerful set of tools for those who can master the demands of python and API interfaces.  In contrast, the approach the NodeXL project has taken is to provide an end-user GUI application environment built within the framework of Excel 2007 for performing basic social media network analysis and visualization for non-programmers.  The python path is certainly the high road for experts and those with demanding volumes or esoteric data requirements.  But for the non-coding user, NodeXL may be one of the easiest ways to both manipulate network graphs and get graphs from a variety of social media sources.

There are already some materials available to guide new users interested in learning about NodeXL, social networks, and social media.  A video tutorial for NodeXL demonstrates the extraction of the network of people in twitter who mentioned the term “digg”.  A tutorial guide to NodeXL offers a step by step guide to features in the NodeXL toolkit (with supporting data sets).  But the book will capture the theory, history, domain and process of social media network analysis in a single volume.

The volume contains a broad introduction to social media, social networks and the operation of the NodeXL application and then features a series of  chapters from leading researchers that focus on a particular social media system (email, Facebook, Twitter, YouTube, flickr, Wikis, the WWW hyperlink network) and the networks each contains (replies, friends, follows, subscribes, comments, favorites, edits, links, etc).   A final chapter outlines a programmer’s view of the NodeXL code, in contrast to the code-free approach of the remainder of the book.

Our intended audience is the mostly non-programming population that is interested in social media and the techniques of social network analysis.  The volume is largely in the form of a how-to guide that readers can follow and replicate all examples.  Using your own free and open copy of NodeXL, you will be able to use sample data sets or create similar live queries that map relationships in social media systems.

We have an ambitious production schedule so the book may be on a book store shelf or online retailer search result in summer 2010.

Table of contents…

NodeXL has updated again (v.1.0.1.109) with new network metrics.  The application now calculates path length data for your network, reporting the Maximum Geodesic Distance and the Average Geodesic Distance.  The list of overall metrics NodeXL creates includes: Vertices (the number of nodes in the graph), Unique Edges, Edges With Duplicates, Total Edges, Self-Loops (Edges that point back at the node from which they originate), Connected Components (each set of connected nodes that are not connected to another set of nodes), Single-Vertex Connected Components (all the “singletons” of just one node in a component), Maximum Vertices in a Connected Component (the size of the “Giant” component), Maximum Edges in a Connected Component (the density of the “Giant” component), Maximum Geodesic Distance (Diameter) (the longest path that can be uniquely walked through the graph), Average Geodesic Distance (the average distance between two nodes in the graph (compare this to the “six degrees” standard), Graph Density (the density of the complete network).

More metrics and details on existing metrics are on the way!

What metrics do you need?

NodeXL updated starting with version 1.05 with features that make it fairly easy to create basic “Venn Diagrams”.  A Venn diagram is a familiar way to illustrate the overlap (or lack thereof) of two or more “sets” of things.

There are some very amusing Venn diagrams out there!  This one in particular made me laugh but I may be dating myself.

The Venn diagram feature is a special request from the Microsoft Biological Foundation group.

A Venn is related to but different from an Euler diagram.  An “n-Venn” diagram is a collection of closed curves (“circles”) on a plane where all the circles intersect. A “simple” Venn diagram has just two circles but complex diagrams can have more.  A 2 circle Venn diagram has 3 regions (A, B, A+B) and a 3 circle Venn diagram has 7 regions (A, B, C, AB, AC, BC, ABC).

A Survey of Venn Diagrams can be found at http://www.combinatorics.org/Surveys/ds5/VennEJC.html.

Our implementation is a bit of a hack, we basically let you define the X/Y location of 3 circles.  A richer Venn tool would make it easy to take set data and define these circles.  We may get that implemented in the coming months.

The fellowship is designed to support research in the following areas:

• Internet Economics: auction theory and algorithmic game theory relevant to online advertising auctions.
• Cloud Computing: storage, databases, and optimization for computing in a massively distributed environment.
• Social Computing: models, algorithms and systems around social networks, social media, social search and collaborative environments.
• Data Mining and Machine Learning: learning algorithms, feature generation, and evaluation methods to produce effective online and offline models of behavioral signals.
• Systems: hardware, operating system, runtime, and language support for fast, scalable, efficient data centers.
• Information Retrieval: search algorithms, information extraction, question answering, cross-lingual retrieval and multimedia retrieval

Applications are due on February 15th. For full details, check out the Facebook Fellowship page.

Please pass this information on to any PhD students you think might be interested in applying.

Improvements to these layouts have been slow in coming: the math behind these layout algorithms have no simple or even best solutions.

Recently, a simple technique has done a great deal to improve many complex network graph layouts by arranging each component in the graph in a grid.  Components are pieces of a network that are not connected to any other component.  These islands come in various sizes, often there is one large or giant component and many smaller “isolates”.  In many layout algorithms these isolates are a problem and are either pushed to the edges of the graph into a circle or ellipse that resembles an “asteroid belt” (Fruchterman-Reingold) or are overlaid on top of all the other isolates (Haren-Koren).  A solution is to collect all the “isolates” and organize them sensibly and within a grid such that each component is laid out within its own territory or cell.

*BEFORE*

*AFTER*

The result is a step towards what Ben Shneiderman refers to as “NetViz Nirvana” – a state in which network graphs are more visually intelligible.  When isolates are binned in a grid, two graphs can be visually contrasted far more than when they each have a smeared “asteroid belt” of nodes.

We have implemented an initial binning layout method in the latest version of NodeXL that simply breaks out each component and places it within a grid based on the number of nodes and edges in that sub-graph.  I can imagine more sophisticated approaches would locate each sub-graph based on a range of attributes.

I think the improvement in network visualization is significant.  Isolates no longer impose a big effect on the giant component which often was compacted and compressed as a result of even a single isolate.

There are hundreds of conferences sponsored by the ACM on almost every topic related to computing.  In some cases the same person will publish a paper in more than one conference, creating a tie between them.  Below is a network map application that displays a collection of ACM conferences connected by this authorship tie: http://bosch.informatik.rwth-aachen.de:5080/AERCS/Networks.jsp

The application is a project created by Manh Cuong Pham a graduate student at RWTH Aachen University, Dept. of Databases and Information Systems working with Prof. Ralf Klamma.

This image displays the isolated component that is composed of the “social” conferences in the ACM schedule: CHI, CSCW, DIS, UIST, GROUP, ECSCW, and Interact.  The overview illustrates the macro structure of the graph, with the prominent giant cluster of core computer science topics like algorithms, machine learning, and logic.  The rows below this cluster are populated by an archipelago of conferences, a few composed of ten to twenty conferences, but most made up of two to five conferences.  These are the more marginal topics in the ACM world, in contrast to the conferences at the cores of the giant component.

It would be nice to see the application add additional network display attributes like size, color, shape, edge thickness to indicate conference attributes like papers published, cited, attendees, and sponsors.  It is a nice example of the insights network visualizations can bring to a data set and the value of an interactive interface (and a web interface at that!) for investigating complex graphs.

The deck illustrates the use of NodeXL to extract several social media networks from systems like twitter and facebook to generate maps of communities and identify people and objects in key locations.

Using surnames from users in the United States and comparing the rates at which those surnames occur in several ethnic populations, they were able to estimate the proportion of Facebook users within different populations over time. The graphic shows a time series of the estimated percentage of U.S. Facebook users from each of 4 races/ethnicities, normalized by the expected percentage. In other words, a value of 100% means the population in question has the same proportional representation on Facebook as it does in the census data. A value over 100% means the population is over-indexed among Facebook users, and a value under 100% means the population is under-represented on Facebook. It appears that Facebook has always been fairly diverse, and apart from over-indexing for Asians/Pacific Islanders the user population is now reasonably representative of the Internet using population in the U.S.

For more details on the study, including the estimation and modeling approaches used, check out the original note on the data team page, or Cameron Marlow’s full repost.  Lars Backstrom, Jonathan Chang, Cameron Marlow and Itamar Rosenn conducted this research.

I. Introduction

Introduction to Communities in Cyberspace, Peter Kollock and Marc Smith

“Since 1993, computer networks have grabbed enormous public attention. The major news and entertainment media have been filled with stories about the “information superhighway” and of the financial and political fortunes to be made on it. Computer sales continue to rise and more and more people are getting connected to “the Net”. Computer networks, once an obscure and arcane set of technologies used by a small elite, are now widely used and the subject of political debate, public interest, and popular culture. The “information superhighway” competes with a collection of metaphors that attempt to label and define these technologies. Others, like “cyberspace,” “the Net,” “online,” and “the web,” highlight different aspects of network technology and its meaning, role and impact. Whichever term is used, it is clear that computer networks allow people to create a range of new social spaces in which to meet and interact with one another.”

More details from the book…

II. Identity

Identity and deception in the virtual community, Judith S. Donath, MIT Media Lab

“Identity plays a key role in virtual communities. In communication, which is the primary activity, knowing the identity of those with whom you communicate is essential for understanding and evaluating an interaction. Yet in the disembodied world of the virtual community, identity is also ambiguous. Many of the basic cues about personality and social role we are accustomed to in the physical world are absent. The goal of this paper is to understand how identity is established in an online community and to examine the effects of identity deception and the conditions that give rise to it.”

Reading Race Online: Discovering Racial Identity in Usenet Discussions, Byron Burkhalter (UCLA, Sociology)

Writing in the Body: Gender (Re)Production in Cyber Interactions, Jodi O’Brien (Seattle University, Sociology)

III Social Order and Control

Hierarchy and Power: Social Control in Cyberspace, Elizabeth Reid (Royal Melbourne Institute of Technology, Communications)

Hierarchies and power on MUDs-the text-based multi-player virtual reality games found on the Internet-rely on the control of players’ abilities to manipulate the virtual environment. Social status on a MUD is linked to a player’s ability to manipulate the virtual components of the system; rewards consist of increased access to such world-manipulating tools. In Power/Knowledge Foucault described an effective form of power as one that enables the powerful to “gain access to the bodies of individuals, to their acts, attitudes and modes of everyday behaviour.” On a MUD, where the physical body is not present, but the virtual body is at the absolute mercy of those who control the system, such power exists. The theatre of authority in a MUD is one which demands and facilitates a strongly dramaturgical element. Underlying each MUD system are cohesive social structures which centre on control and the manipulation of game elements. Every piece of information a player integrates into the MUD universe permits and assures the exercise of power. Speaking and writing-transmitting knowledge-are acts of literal power in the virtual reality of a MUD, and permit the creation of hierarchies of social control.

Problems of Conflict Management in Virtual Communities, Anna DuVal Smith (Case Western Reserve University, School of Management)

This paper explores the sources of conflict and techniques of social control in an open-access, text-based virtual community. It argues that such social systems have the same kinds of opportunities and problems brought by diversity that real communities do, but that unique features of cyberspace make effective conflict management both more important and more difficult. Cases of interpersonal disputes collected during 22 months of participant observation revealed that power strategies of social control were generally counterproductive in managing the conflict that resulted from the multiplicity of values, goals, interests and cultural norms brought by members of the community. As in real life, methods that reconcile divergent interests mediation and factfinding) and adjudicate rights (factfinding and arbitration) appeared to manage issue-based conflicts more effectively. However, their utility and, therefore, the community’s ability to adapt and thrive as an open, goal-directed system depends on member awareness of the program, human resource availability and administration willingness to share power.

IV Community Structure and Dynamics

Net Surfers Don’t Ride Alone: Virtual Communities as Communities, Barry Wellman & Milena Gulia (University of Toronto, Sociology)

Invisible Crowds in Cyberspace: Measuring and Mapping the USENET , Marc Smith (UCLA, Sociology)

The Economies of Online Cooperation: Gifts and Public Goods in Cyberspace, Peter Kollock (UCLA, Sociology)

V Collective Action

The Promise and the Peril of Social Action in Cyberspace: Ethos, Delivery, and the Protests over MarketPlace and the Clipper Chip, Laura J. Gurak (University of Minnesota, Rhetoric)

In April 1990, Lotus Development Corporation announced a product called MarketPlace: Households. MarketPlace was to be a direct mail marketing database for Macintosh computers and would contain name, address, and spending habit information on 120 million individual American consumers. After MarketPlace was announced, computer privacy advocates began investigating the product. Although most of the data contained in MarketPlace was already available (data was provided by Equifax, the second largest credit reporting agency in the United States), privacy advocates felt that MarketPlace went beyond current standards for privacy protection. Having the data so readily available to a mass market of personal computer users extended the existing network of information sources in the US, including credit profiles, grocery store checkout scanning systems, and government files. Furthermore, the data was provided on the non-correctable media of CD-ROM; therefore, if an entry was in error, it could not be corrected. And although Lotus did include certain privacy protection measures when designing the product, privacy advocates were not convinced.

From Lotus’s first announcement until months after it canceled the product, the Internet was full of discussions about MarketPlace; soon, debates about the privacy implications of MarketPlace and suggestions for contacting Lotus began to circulate. People posted Lotus’s address and phone number, the email address of Lotus’s CEO, and also gave information about how to request that names be removed from the database. Some people posted “form letters” that could be sent to Lotus. Notices were forwarded around the Internet, re-posted to other newsgroups, and sent off as email messages. In one case, a discussion group was formed specifically to discuss the product. As a result of the Internet-based protest, over 30,000 people contacted Lotus and asked that their names be removed from the database. The product, which had been scheduled to be released during the third quarter of 1990, was never released. In January 1991, Lotus issued a press release announcing that it would cancel MarketPlace: Households. In the end, many acknowledged the role of networks in stopping the release of MarketPlace. Some subsequently called it “[a] victory for computer populism” (Winner).

Electronic Homesteading on the Rural Frontier: Big Sky Telegraph and its Community, Willard Uncapher (University of Texas at Austin, Communications).

This chapter provides an overview of an ethnography exploring the introduction of a low cost computer mediated conferencing and communication system into the rural Montana one room school system and develops a framework based on the conflicts between the material economy using information technology to organize global economies of scale and an online gift economies and social scale to explore the informatization of rural communities. The paper proposes that we should not limit our analysis of online communities simply to online behavior.

Based on interview, site visits, and an extensive collection of secondary materials, primarily between January 1988 when Big Sky Telegraph first went online, and January 1990, the research provides a case study of the way social, cultural, economic, and pre-existing communication arrangements come to frame the uses of the new technology, even as they are transformed by them. Not all communities are transformed equally, and those users, such as rural teachers and women wanting to change their lives provided a base of new users, while the ranching and farming community was more circumspect. The social differences would impact not only online behavior, but how behavior gets online, and even how the borders between these worlds are imagined.

Cyberspace and Disadvantaged Communities: The Internet as a Tool for Collective Action, Christopher Mele (State University of New York at Buffalo, Sociology)

This chapter recounts the collective action of an organized group of African-American women residents of Jervay Place, a low-income housing development in Wilmington, North Carolina, and their use of online communication to attempt to challenge not only the immediate issue of adequate housing but their position within the unequal power relationship between African-American women and white elite-dominated institutions. In the case of Jervay, collective action emerged in response to the housing authority’s exclusion of residents as agents in the planning for the site’s future. Their efforts at mobilization and resistance and the reactions of the housing authority were particularly embedded in the local and historical context of power and race. Ultimately, in order to challenge the unilateral decisions of the housing authority effectively, resident determined it was necessary to circumvent the local customary (paternalist) forms of interaction that have long existed between disenfranchised African-Americans and local political and social institutions. Use of online communication afforded the women an opportunity to operate outside the local and exclusive pathways of information, discourse and social action controlled by the institution of the housing authority. In a broader sense, their grassroots networking activities online subverted longstanding local articulations of power. The chapter concludes with a discussion of the implications of online communication for mediation of historically unequal relations between disadvantaged groups and social institutions.

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]]> http://www.connectedaction.net/2009/12/15/book-communities-in-cyberspace-ten-years-later/feed/ 4 http://www.connectedaction.net/2009/12/14/call-for-papers-icwsm-2010-washington-d-c-may-23-26/ http://www.connectedaction.net/2009/12/14/call-for-papers-icwsm-2010-washington-d-c-may-23-26/#comments Tue, 15 Dec 2009 00:00:36 +0000 Marc Smith http://www.connectedaction.net/?p=1821 Here is the Call for Papers for the

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

IMPORTANT DATES:
Tutorial Proposals: December 1, 2009
Paper Submission: January 8, 2010
Poster/Demo Submission: January 8, 2010
Paper Acceptance: March 3, 2010
Poster/Demo Acceptance: March 3, 2010
Workshop Submission: March 1, 2010
Camera Ready Copies: March 12, 2010

Featuring a keynote by:
Professor Bob Kraut, CMU,
on “Designing Online Communities from Theory”

Professor Michael Kearns, Computer and Information Science,
Univ. of Pennsylvania,
on “Behavioral Experiments in Strategic Networks”

Speakers in Special Sessions:
- Nicole Ellison, Dept. of Telecommunication,
Information Studies and Media, Michigan State Univ.
- James Pennebaker, Dept. of Psychology, Univ. of Texas, Austin
- S. Craig Watkins, Dept. of Radio, TV and Film, Univ. of Texas, Austin- Don Burke, CIA Directorate of Science and Technology, Intellipedia
- Haym Hirsh, National Science Foundation IIS Division Director
- Macon Phillips, U.S. White House, Head of New Media

Tutorial Speakers will include:
- Jake Hofman, Yahoo! Research,
“Large-scale social media analytics with Hadoop”

- Cindy Chung and James Pennebaker, Univ. Texas,
“Using LIWC to uncover social psychology in social media”

TECHNICAL AREAS
More specifically, ICWSM welcomes submissions from researchers in a number of disciplines:
- Computational Linguistics/NLP
- Text Mining/Data Mining/Machine Learning
- Psychology
- Sociology (including Social Network Analysis)
- Anthropology, Communications, Media Studies
- Visualization
- HCI
- Graph theory, concrete analysis and simulation of graphical models

Submissions are welcome that study a broad array of types social data, including:
- Weblogs, including comments
- Social Networking Sites
- Microblogs
- Wikis (wikipedia)
- Forums, usenet
- Community media sites: youtube, flickr

Technical topics of interest include:
- Psychological, personality-based and ethnographic studies of social media
- Analyzing the relationship between social media and mainstream media
- Qualitative and quantitative studies of social media
- Centrality/influence of social media publications and authors
- Ranking/relevance of blogs; web page ranking based on blogs
- Social network analysis; communities identification; expertise and authority discovery; collaborative filtering
- Trust; reputation; recommendation systems
- Human computer interaction; social media tools; navigation and visualization
- Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction
- Text categorization; topic recognition; demographic/gender/age identification
- Trend identification and tracking; time series forecasting; measuring predictability of phenomena based on social media
- New social media applications; interfaces; interaction techniques

SUBMISSION
People interested in participating should submit through the ICWSM-10 website a technical paper (up to 8 pages, not including references),poster or demo description (up to 4 pages) by the deadlines given above (Midnight PST). Papers must be must be formatted in AAAI two-column, camera-ready style (see the AAAI author instructions page at http://www.aaai.org/Publications/Author/author.php). Details for the submission procedure will appear at the conference website: http://icwsm.org

PUBLICATION
All accepted papers and abstracts will be allocated eight (8) pages in the conference proceedings. Authors will be required to transfer copyright of their paper to AAAI.

DATA CHALLENGE
ICWSM-10 will once again hold a data challenge featuring a freely-available dataset and a half-day workshop at the conference. Details will be posted on the conference website.

CONFERENCE WEBSITE
www.icwsm.org

For general information regarding ICWSM-10, please write to icws…@aaai.org. More details about the CFP and the conference will appear on the website over time.

ORGANIZERS:
Program Chairs:
William Cohen, CMU Computer Science
Samuel Gosling, U Texas Dept of Psychology

General Chair:
Marti Hearst, UC Berkeley School of Information

Senior Program Committee Members:
Lada Adamic, Univ. of Michigan
danah boyd, Microsoft Research
Claire Cardie, Cornell Univ.
Kathleen Carley, Carnegie Mellon Univ.
Chris Diehl, Lawrence Livermore National Labs
Nicole Ellison, Dept of Telecommunication, Information Studies, Michigan State University
Lise Getoor, Univ. of Maryland
Jure Leskovec, Stanford Univ.
Winter Mason, Yahoo! Research
Kate Neiderhoffer, Dachis Corporation
Eugene Agichtein, Emory Univ.
Cindy Chung, Univ. of Texas at Austin
Scott Counts, Microsoft Research
Tim Finin, UMBC
Evgeniy Gabrilovich, Yahoo! Research
Kristina Lerman, ISI-USC
Jure Leskovec
Winter Mason
Gilad Mishne, Yahoo! Labs
Bo Pang, Yahoo! Research
Marc Smith, Connected Action Consulting Group

NodeXL has had a rudimentary flickr tag network data spigot for some time but we have just added a number of features to this data importer that makes it much more useful.

You can now select the number of network levels to include, an optional sample image file can be included for each tag, and the dialog now provides feedback as it requests the various parts of the network from Flickr.

The tag network generates maps like the following set of connections among terms related to “sociology”:

We  discussed ways to leverage social networks  networks beyond personal connections – to provide business value.  We talked about ways to efficiently and effectively use social media to market and grow your business.

Using social media to grow and market your business

I spoke about tools, like NodeXL, that we have been building that create maps of the relationships among a population of people gathered by some shared attribute, like mentioning a keyword or hashtag.

Here are a few excerpts from the event created by Tendo Communications:

The Second Conference on Online Deliberation: Design, Research, and Practice (OD2005/DIAC-2005) was held at Stanford University May 20-22, 2005. From that event there is now a book,  Online Deliberation: Design, Research, and Practice, edited by Todd Davies and Seeta Peña Gangadharan (CSLI Publications, November 2009).  All content in the book is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.

I will call out a few of the many interesting chapters, one of which I contributed to:

Chapter 5: Friends, Foes, and Fringe: Norms and Structure in Political Discussion Networks (John Kelly, Danyel Fisher, and Marc Smith, pp. 83-93)

And two from colleagues who report on tools for facilitating political debate and decision making:

Chapter 6: Searching the Net for Differences of Opinion (Warren Sack, John Kelly, and Michael Dale, pp. 95-104)

Chapter 26: Online Civic Deliberation with E-Liberate (Douglas Schuler, pp. 293-302)

The book is a great guide to the many ways computer-mediated interaction technologies are being used to build consensus or tear it apart!

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NodeXL network of related YouTube videos sharing the term "blend".

Starting in version 100 NodeXL has added a data import feature for extracting social networks from the associations between users and videos in YouTube.  The new social media network data spigot offers insights into the ways YouTube is socially structured.  This spigot joins the existing twitter, flickr, and email data import providers present in NodeXL.  We plan to deliver a expanded flickr spigot soon and have been working with others to deliver a hyperlink and wiki network provider in the coming months.  We have an interest in social media network data spigots for other networks data sources like SharePoint, Exchange, and LDAP servers.

There are two types of network that NodeXL can extract from YouTube: video and user networks.

Video networks are collections of related videos linked by a shared string in their title, keywords, description, categories, or author’s username.

The user network returns a set of YouTube user names and the links between them based on users friending and or subscribing to another user.

Each import dialog allows for a number of configurations over the volume and detail of the data set requested.

Registration: http://smanalysistwitter.eventbrite.com/

For example, this is a map of the population of people who recently mentioned “NodeXL” in Twitter.

I see a large cluster of French speaking people who recently picked up on a tweet:

seekoeur: [Blog] Identifier des communautés et cartographier des relations sur Twitter avec NodeXL http://www.seekoeur.com/?p=854

This is a very distinct group from the second cluster around my own account and the recently created NodeXL twitter account.

In contrast, this is the map of all the people who mentioned the keyword “SharePoint” recently in Twitter.

This is clearly a much larger population and one with a dense core of highly connected individuals.  A number of peripheral groups surround the core.  Drilling in can reveal who the center of the center of the network is, a potentially highly influential person.

After visiting the Oxford Internet Institute on December 4th, I will be visiting Aachen University to talk about social media network analysis this December 7th and 8th.

I will be visiting with my hosts, Zinayida Petrushyna and Ralf Klamma who have been doing insightful work exploring social media sites like Media Wikis and Wikipedia.  The Wiki Watcher project is a big favorite of mine.  Their working group on Social Network Analysis on Dynamic Digital Networks is of particular interest.

While I am back in Aachen, I hope I will get to see some of the folks I met at the “European Microsoft Innovation Center” (EMIC) located there.  They focus on research into embedded systems which I see as having vast implications for the ways computers sense social relationships.  When sociologically aware machines are widely used, we will have new ways of building and maintaining relationships.

I will give three presentations over two days in Aachen – I hope you can attend if you are in the area!

Monday, December 7, 2009 (in cooperation with the Web Science II course at RWTH Aachen University):

12:15 – 13:45 Social Media Visualization Analysis: overview of work to visualize social media using treemaps, histograms, scatterplots and social network diagrams

Using social media makes evidence of social relationships into machine readable data streams.  The resulting data sets contain time series, hierarchy, and network data structures that can be visualized to illustrate the range of variation in social media data spaces and participants.  Histograms, line charts, treemaps, and network diagrams can be used in concert to illuminate the many facets of behavior and population present in social media spaces.  Resulting images illustrate the range of variation of individual and collective formations in social media spaces.

16:00 – 17:30 NodeXL – A hands-on guide: a workshop style review of sample data sets that are processed through NodeXL operations to generate metrics and a visualization that tells a story.

As social media networks proliferate there is a growing need for tools to manage, analyze, and visualize network graphs.  NodeXL is an add-in for Office 2007 that provides social network diagram and analysis tools in the context of a spreadsheet application. Adding the directed graph chart type to Excel opens up many possibilities for easily manipulating networks and controlling their display properties. In this workshop the steps needed to install and productively operate NodeXL for Office 2007 are reviewed. The free and open NodeXL add-in provides directed graph charting features within Excel, allowing users to create node-link diagrams with control over each node and edge color, size, transparency and shape without requiring the use of a command line interface or programming language. Since NodeXL builds within Excel, all of the controls and programmatic features of Office are available. Additional features of NodeXL generate social networks from social media data sources like personal e-mail (drawing data from the Windows Desktop Search engine) and the Twitter social network micro-blogging system. Arbitrary edge lists (anything that can be pasted into Excel) can be visualized and analyzed in NodeXL. This session will provide a walk through the basic operation of NodeXL.

Tuesday, December 8, 2009

10:00 – 11:00 In the future my phone will notice your phone: from ephemeral to archival societies

New sources of data from everyday life are being captured and recorded with mobile devices, creating a new stream 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 map their internal social network structure as an accidental by-product.  Studying these data is sets is a focus of growing interest. Research projects like SenseCam are now becoming products and services like nTag, Spotme, Fire Eagle, and Google Lattitude using devices like iPhone and G1 are weaving location into every application.   When my phone notices your phone a new set of mobile social software applications become possible that capture data about other people as they beacon their identifies to one another. Additional sensors will collect medical data to improve our health and safety, as early adopters in the “Quantified Self” movement make clear.  Social media systems are being linked to one another to enable cascades of events from a single message as status updates are passed among Facebook, LinkedIn, Twitter, and blogs 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.  The result will be lives that are more publicly displayed than ever before.  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.  Risks include more efficient state and corporate surveillance and self-imposed censorship.

On December 4th, I will be attending the Oxford Internet Institute’s Forum on Relationships and the Internet which will feature researchers focused on computer-mediated relationships.  Rather than being used primarily to access information, people often use networked computing to access people, creating or sustaining relationships with others.  There are many ways in which the Internet and a range of applications are now used to find new people, find old people, maintain primary connections, sustain weak ties, and build personal and professional relationships.  Dating sites are a clear part of the Internet relationship picture but so are professional “dating” services like LinkedIn or professional services directories like Craig’s List, social networking services like Facebook, and “normal” tools like email.  People now use a concerted set of Internet tools in building and maintaining their relationships.

Researchers are interested in online dating and social networking.  They are now exploring the role of information and communication technologies in all forms of interpersonal relationships.  Practitioners from a growing and international relationship industry who want to cultivate and manage these relationship systems will attend along with policy-makers who seek to regulate their uses towards positive ends and avoid abuses.

Speakers include:

• Bill Dutton, Oxford Internet Institute
• Nai Li, Oxford Internet Institute
• Bernie Hogan, Oxford Internet Institute
• Ellen Helsper, London School of Economics
• Monica Whitty, Nottingham-Trent University
• Barry Wellman, University of Toronto and Director of NetLab
• Joe Walther, Michigan State University
• Nicole Ellison, Michigan State University

Research in Internet relationships goes beyond a focus on dating sites and services to incorporate all the stages and types of relationships being shaped by social information technology.  My demonstration will illustrate the ways NodeXL can map relationships in a variety of social media spaces, including YouTube, flickr, email, twitter, and (with the help of Bernie Hogan’s ego-network importer) Facebook.  These maps can be a useful ways of distinguishing different kinds of relationship patterns present in these spaces, identifying key participants who acts as hubs or bridges.

The OII also maintains a video webcast site with a great collection of recordings of lectures and panel discussions of a variety of  topics related to the Internet and society.  In particular, check out Duncan Watts presenting a talk on “Using the Web to do Social Science“.

2009 – November – NodeXL – Demo – Mapping Twitter Social Networks “Digg” from Marc Smith on Vimeo.

This is a good demonstration of several features in NodeXL: social media network importers (in this case from Twitter), the use of a variety of layouts, auto-fill column mappings, and dynamic filters to reveal some important structures, groups and people in the graph.

Here is the source data in a NodeXL workbook: 2009 – November – NodeXL – Twitter Digg 3

Note!  Excel has issues with security and workbooks.  The easiest way to use that file is to open a blank NodeXL template and use the “Import from older workbook ” feature to pull the data into a workbook that your copy of Excel *can* trust.

http://books.google.com/books?id=v4A39158MUQC

At the core of her extensive, empirical, research into the ways humans share common goods and resources is a set of eight core design principles.  These “8″ principles for successful creation and consumption of a resource are documented in the book “Governing the Commons”

Next week I will be attending and discussing mobile social media and social networks at the Mobile Web Africa conference in Johannesburg, South Africa. It is my first time to Africa and I am excited to both visit and to discuss how mobile networked devices can change social organizations.  Mobile devices are in many ways more important in emerging and developing regions where the availability of these tools enable the first voice and data services that have ever been affordable and reliable, let alone mobile.  There are vast opportunities in this space, a topic well reviewed in the recent issue of the Economist:

The Mobile Web Africa event will focus on these key questions:

1. How will the industry evolve to a point where the vast majority of people have access to the mobile web and the content they want to view?
2. How will the industry fully exploit existing and future opportunities?
3. How can PC or mobile based developers and start-ups monetise their innovation and creativity to grow in to companies that will drive the expansion of the ecosystem?
4. How can Operators, Original Equipment Manufacturers, global associations and other mobile powerhouses assist their smaller partners?
5. How can societal and economic problems be tackled by the development of the capabilities of the mobile device?
6. What handsets, standards, networks and designs will allow consumers to successfully access the content and consume it?
7. How will the consumer be able to discover that content – through an Application, Browser, Search Engine, Advert, Social Network?

I will focus my discussion on the idea of the “Electrification of the Interaction Order” and topics related to the growing use of sensors on mobile devices and the sharing of the resulting data.  A service like SenseNetworks is a  good example of a mobile data collection, analysis, and presentation service.  Other sites, like Quantified Self, CureTogether, and FitBit, are examples of the social movements, web applications, and devices that are emerging in the self-monitoring medical tracking space.  These communities overlap with the trail based exercise communities of runners, bikers, skaters, hikers, and skiers, some with artistic inclinations.  I see a new wave of devices that are extensively quantify your “self” and “others”, perhaps when people swap sensor data with one another.  The recent work of Nathan Eagle and his co-authors illustrate the possibilities of using many devices with (already existing and widely used) sensors can generate remarkable maps of human behavior.  Much of this data will take the form of social networks as people are linked by “hyperties” – forms of association and connection that are authored by machines from the records of association and behavior.  People will be linked who have never met, in the same way that web book store customers who have never met can be linked by common browse and purchase patterns.  Hyperties will be formed by shared use of location, even if at different times, or patterns created by passing through different spaces at different times but in common patterns (Starbucks, then gas?  Or subway then tea shop?).  The notion of “Tribes” used by the SenseNetworks company is a good example of this approach.    The Economist is all over this topic, with another article “Mobile phones Sensors and sensitivity” that captures the topic.  Jonathan Donner’s work is also a good resource for insights on the role of mobile technology in many parts of the world.  The ability to enable a form of banking service is a particular benefit for the many people who do not have access to banking services.

What social media data most interest you?  We are considering integration with web and wiki crawlers, and support for YouTube, delicious, and enterprise data sources like Active Directory (LDAP), SharePoint, and Exchange.

This release also improves support for images, particularly those pulled from URLS, like twitter or facebook profile photos!

Here, for example, is a map of the connections among twitter accounts that tweeted the “WIN09″ tag that was used in the recent Social Networks Summit at NYU (http://winworkshop.net/) The map illustrates the way the summit brought together previously separate clusters of people from the various disciplines that have been attracted to the study of networks in general and social networks in particular.  Size of the image equals the number of tweets that person created.

A refined version adds Edge Labels and color to highlight the different tie types in the graph: “follows” relationships and “replies to” and “mentions” and now scaled by “Followers”.

In both views, the high betweenness role of one twitter account is clear.

Release details below the fold….

1.0.1.95 (2009/09/28)

• When an email network is analyzed (NodeXL, Data, Import, From Email Network), the resulting graph is now directed. This means that the relationships (John,Mary) and (Mary,John) are no longer combined into a single edge with an edge weight of 2; instead, they are considered unique edges.

1.0.1.94 (2009/09/28)

• When importing a Twitter search network (NodeXL, Data, Import, From Twitter Search Network), you can now add a Tweet column to the Vertices worksheet.
• When importing a Twitter user network (NodeXL, Data, Import, From Twitter User Network), you now have some options for how edges get added to the graph. You can add an edge for each followed/following relationship (which was always done in previous versions), each “replies-to” relationship in the people’s latest tweets, and each “mentions” relationship in the latest tweets.
• Ditto for the Twitter search network, although in this case the “replies-to” and “mentions” relationships apply to the tweets that satisfied the search criteria, which aren’t necessarily the people’s latest tweets.
• Twitter search networks and Twitter user networks now add an Image URL column to the Vertices worksheet. The images are those of the people who wrote the tweets.
• Bug fix: In the Import from Twitter Search Network dialog box, the word “latest” was removed from the text, “Search for people whose latest tweets contain…” Reason: The tweets returned by the search aren’t necessarily the people’s latest tweets.
• Bug fix: When importing a Twitter search network, a tweet posted by a person with “protected” status in Twitter would bring the search to a halt with a “There is no Twitter user with that screen name” error. Now, such people are skipped.
• Bug fix: Using any of the NodeXL, Data, Import items in the ribbon failed to clear the Images worksheet before importing the new data.

1.0.1.93 (2009/9/18)

• You can now import a Twitter network of users who have tweeted a specified term. For example, you can create a graph with a vertex for each person who has included the hashtag “#chi2010″ in a tweet, with an edge between the people who follow each other. In the Excel Ribbon, go to NodeXL, Data, Import, From Twitter Search Network.
• When including an image in a graph, you can now specify an URL to an image on the Internet.
• When an image file isn’t available, an error message is no longer displayed. Instead, a small red X is shown in the graph in place of the missing image.
• Images can now be resized using the Size column on the Vertices worksheet. There are new options in the Options dialog box for setting the default image size. (Known bug: Changing the default image size doesn’t update the graph pane until the workbook is refreshed.)
• When importing from a Twitter user’s network (NodeXL, Data, Import, From Twitter User’s Network), a Relationship column is added to the Edges worksheet. This gets set to Followed or Follower.
• The Twitter dialog boxes now provide feedback on what they’re doing as they assemble the requested network.
• Bug fix: Attempting to get a Twitter user network that included someone who “protected” her Twitter identity would cause a failure. Now, that user is skipped and the rest of the network is obtained.
• Bug fix: There was a rounding error with very small numbers (on the order of 1.0E-22) that could cause some vertices or edges to always be filtered out by Dynamic Filters, even if the filters were reset to their entire range.

1.0.1.92 (2009/9/4)

• In the Autofill Columns dialog box (NodeXL, Visual Properties, Autofill Columns), you can now specify a logarithmic mapping instead of a linear mapping when autofilling Edge Color, Edge Width, Edge Opacity, Vertex Color, Vertex Size, Vertex Opacity, Vertex Primary Label Fill Color, Vertex Layout Order, Vertex X, Vertex Y, Vertex R, or Vertex Polar Angle. Click one of the Options buttons, then check “use a logarithmic mapping.”
• You can now import a network of Flickr tags related to a specified tag. Use NodeXL, Import, From Network of Related Flickr Tags. You will need what Flickr calls an “API key.” There is a link in the Import dialog box for requesting a key from Flickr.
• The Twitter import feature has been expanded. (It’s at NodeXL, Import, From Twitter User’s Network.) You can now import the network of people followed by a user, people following a user, or both. There is a new option for selecting a 1, 1.5, or 2-level network, and you can limit the network to a specified number of people. New columns are added to the Vertices worksheet: Followed, Followers, Tweets, and (optionally) Latest Tweet. Also, you can right-click a vertex in the graph pane and select the new Open Twitter Page for This Person menu item.
• Bug fix: When using dynamic filters, a filtered edge’s label obscured what was under it even though the edge itself was hidden.
• For programmers only: The IGraphDataProvider interface used by data provider plug-ins for the Excel Template has changed. The GetGraphData() method is now called TryGetGraphData() and it now returns a Boolean. (The original design failed to accommodate failures while getting graph data.)

1.0.1.91 (2009/8/19)

• You can now label edges by filling in a new Label column on the Edges worksheet. The Label column is hidden by default. To make it visible, use NodeXL, Show/Hide, Workbook Columns, Labels in the Excel Ribbon.
• You can now import a graph from a GraphML file. GraphML is an XML-based file format used by a variety of graph applications and libraries, including Pajek, “R,” and JUNG. NodeXL will import all edge and vertex attributes in a GraphML file, including those that correspond to standard NodeXL columns such as Edge Color and Vertex Size. Use NodeXL, Data, Import, From GraphML File.
• Importing from a Twitter network (NodeXL, Data, Import, From Twitter Network) is now more reliable, thanks to automatic retries that will occur if a request to Twitter fails.
• Of possible interest to developers: NodeXL now supports custom “plug-in” .NET assemblies that will import graphs from custom data sources. For details, see the IGraphDataProvider Interface topic in the NodeXLApi.chm help file and the sample implementation in SampleGraphDataProvider.cs.
• The graph legend is now more compact. (To show the legend, use NodeXL, Show/Hide, Graph Elements, Legend.)
• Bug fix: When importing from an open edge workbook (NodeXL, Data, Import, From Open Edge Workbook), columns in the open edge workbook that have the same name as standard NodeXL columns will be copied to those NodeXL columns. Before, a column named “Color” was copied to “Color 2,” for example.
• Bug fix: Your settings, such as those entered in the Options dialog box, are now stored in a single file in your local Windows profile. Before, each named NodeXL workbook got its own settings file.

1.0.1.90 (2009/7/24)

• When you import data into the workbook (NodeXL, Data, Import), you now have the option to append the imported data to the workbook’s contents instead of clearing the workbook first. Check or uncheck the NodeXL, Data, Import, Clear NodeXL Workbook First checkbox to control this.
• The Closeness Centrality graph metric (NodeXL, Analysis, Graph Metrics) is now computed much more quickly. For example, with a graph containing about 1,000 vertices and 1,000 edges, the computation time went from 31 seconds to 3 seconds, and with a larger graph containing 5,000 vertices and 8,000 edges, the time went from 63 minutes to 2 minutes.
• The graph legend is now hidden by default.
• Your settings for showing or hiding the graph legend and axes (NodeXL, Show/Hide, Graph Elements) are now saved along with the rest of your settings.
• If you autofill the X and Y columns in the Vertices worksheet (NodeXL, Visual Properties, Autofill Columns), the Locked column is no longer automatically set to Yes. Instead, the Layout (NodeXL, Graph, Layout) is set to None, which achieves the same effect but is easier to undo. If you no longer want the autofilled X and Y values, just set the Layout to something else.
• In the options dialog boxes within the Autofill Columns dialog box, there is now a “Swap” button that will quickly swap the colors or numbers you are autofilling.
• You can now change the font used for the graph axes. In the graph pane, go to Options, Axis Font.
• The Auto Refresh checkbox that used to be in the NodeXL, Visual Properties Ribbon group is now in the Options dialog box, reachable from the graph pane.
• Bug fix: In the Import From Twitter Network feature, the screen name and password were not being correctly sent to Twitter. This caused Twitter rate limiting to kick in even if your rate limit had been lifted by Twitter.

1.0.1.89 (2009/7/9)

• There are now X and Y axes in the graph pane. To show them, check the NodeXL, Show/Hide, Graph Elements, Axes checkbox in the Excel Ribbon. If you autofill the X and Y columns in the Vertices worksheet (NodeXL, Visual Properties, Autofill Columns), the axes will show the range of autofilled values. Otherwise, the axes simply show NodeXL’s full range of coordinate values (0 to 9,999).
• You can now export the NodeXL workbook’s edges to a UCINET file. The file format is what UCINET calls “full matrix DL.” Go to NodeXL, Data, Export, To UCINET Full Matrix DL File in the Ribbon.
• You can also import a UCINET full matrix DL file. Go to NodeXL, Data, Import, From UCINET Full Matrix DL File in the Ribbon. If you have a file in a different UCINET format, you will need to use the UCINET program to convert it to full matrix DL. Click on the “What if my file is not in full matrix DL format?” link in the Import dialog box for instructions.
• You can now export the NodeXL workbook’s edges to a Pajek file. Go to NodeXL, Data, Export, To Pajek File in the Ribbon. The vertex coordinates are exported, but no other edge or vertex attributes are.

On October 29th, I will be offering a workshop in Mountain View, California on the application of social network analysis to the measurement of social media.

The workshop will run from 9m to 4pm and include hands-on exercises using real world social media data sets and the free and open NodeXL social network analysis add-in for Excel 2007.  We will create social network metrics and visualizations from personal email, twitter, facebook, and message board records to reveal the broad outline of a community, its various kinds of leaders and active participants, major cliques and clusters, and pivotal events.

The workshop will make use of the NodeXL tutorial:
http://casci.umd.edu/NodeXL_Teaching

Registration at:
http://www.eventbrite.com/event/386418789

I will post the slides for those who cannot attend, but the live event will allow me to help those interested in learning how to visualize social media networks and generate and interpret social network metrics.  What’s an “eigenvector centrality”?  Come find out why that number highlights special people in a network and how to calculate it on your own network data sets.  Find experts, identify the people who are most heavily connected, and key contributors. If you plan to attend, it would be great if you bring sample data: any edge list or matrix is fine.  We can plot and measure sample data participants bring along with them.

I will demonstrate how to create twitter network maps like the one above which shows networks of follows connections among a group of people who tweeted the string “#CHI2010″ (as returned by search.twitter.com).  You can make your own twitter maps with NodeXL!  Similar maps can be made with a user name. In the workshop we will be sure to make a twitter network for anyone there who tweets.

Upon completion of 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 sets.
* 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.

Who should attend? People interested in community management, social media monitoring and marketing, knowledge management, collaboration and human resources, legal discovery, organizational behavior and management

Registration.

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Other Maps:
Yahoo | Mapquest | Microsoft

Readers Who Like This Apocalypse Also Preferred …
The end-of-America social network.

Slate’s “Choose Your Own Apocalypse” game asked readers to browse
through a list of 144 end-of-America scenarios and choose up to five
that seem most likely to wipe the United States off the map. The
scenarios our readers chose fell into patterns. Those who chose
“Laziness,” for example, were also likely to choose “Decadence” and
“Overpopulation.” Those who selected “Theocracy,” meanwhile, were also
likely to choose “Intelligent Design.”

To help visualize those correlations, we’ve arranged the scenarios
into a social network. Two scenarios are connected if at least 5
percent of users voted for the pair as part of their end-of-America
menus. The less-popular scenarios on our list of 144 tended not to be
associated with any others. What you see here, then, are the 82 that
have at least one connection to another potential America-ending
scenario. Mouse over any icon to see its title and connections.

More

Prof. Jen Golbeck and Justin Grimes analyzed 6,000 tweets from United States Congress members.  They found some interaction but a dominant broadcast pattern of use with a focus on self-promotion.  The Washington Post headlines the results as “Tweeting Their Own Horns Study Finds Posts By Lawmakers Boastful or Boring”

Here is the video:

From the UMD News desk:
“A new study by University of Maryland researchers finds a
growing use of Twitter among members of Congress, but that
the purpose and content of their messages fall short of
improving government transparency.

Jennifer Golbeck, assistant professor in the College of Information
Studies, Maryland’s iSchool, a doctoral student and an undergraduate
assistant analyzed more than 5,000 tweets sent by 69 members of
Congress in February. They found that House and Senate members
were using the social media platform mostly to promote themselves,
rather than engage in dialogue with constituents and the public at large.

“Members of Congress were not sharing much new information on
Twitter, and there were few posts that improve transparency,”
Golbeck says.”

Tweet the Debates: Understanding Community Annotation of Uncollected Sources

Authors: Shamma, D.A.; Kennedy, L.; Churchill, E.F.
Source: ACM Multimedia, ACM, Beijing, China (2009)
URL: http://www.acmmm09.org/
Keywords: Twitter; Debates; TV; community; multimedia; social; centrality; network

Download: wsm01a-shamma.pdf

At the core of the network are the entities that have the most “eigenvector centrality” which turn out to be the major participants in the debate.

The video is a hoot (but seems to go blank after about 4 minutes).  This looks like a great start towards an interactive web canvas for network graphs.

“To use this interactive tool, just click on a circle to see which stories mention that topic and which other topics it connects to in the network. You can use the magnifying glass icons to zoom in and out. You can also drag the dots around if they overlap.”

It is a lot of fun to play with!  That said, they layouts have limited meaning, using a force directed layout to place the nodes on the canvass.  The color and size indicators are useful but Up/Down and Left/Right have no meaning.

Here is an example: http://twitpic.com/9rvfq

It provides a good illustration of the ways a person’s social network is clumped into clusters built around life phases, workplaces, educational institutions, teams and locations.  As people move through more of these stages of life during the Facebook era (and often before) they accumulate these clusters.

Facebook or other contacts and friend management systems might want to leverage this clustering to organize the presentation of contact information streams.

Bernie just announced on the SOCNET list that he has updated his script for downloading your Facebook network to GUESS or UCInet file formats.
http://apps.facebook.com/mynet_phaseone/

“Features:

1. Its faster. (Presently orders of magnitude faster than Nexus, Touchgraph or ORA).
2. It gives nice feedback during the download.
3. It has less bugs!
4. It gives you the output as a file you can right-click and save rather than copy-paste.
5. IDs are names.”

Bernie writes that phase two of his project is underway.  You can see Bernie’s page describing phase two at the following link: http://mysocnet.oii.ox.ac.uk/fb/namegen4preview.php

Bernie is planning a January 2010 release, and plans to demo at the Sunbelt social network analysis conference in Italy in 2010.

This weekend is the Social Computing 2009 conference in Vancouver, B.C.  It is a gathering of many people doing research on social media useage.  Many papers are about tagging systems, blogs, wikis, message boards, and social networking services.

Along with several co-authors, I contributed to three papers in this year’s conference:

Bonsignore, E.M., Dunne, C., Rotman, D., Smith, M., Capone, T., Hansen, D.L. & Shneiderman, B. (2009), “First steps to NetViz Nirvana: evaluating social network analysis with NodeXL”, In SIN ‘09: Proc. international symposium on Social Intelligence and Networking. IEEE Computer Society Press.

Abstract: Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sensemaking for students of SNA.

Abstract: Broadening adoption of social media applications within the enterprise offers a new and valuable data source for insight into the social structure of organizations. Social media applications generate networks when employees use features to create “friends” or “contact” networks, reply to messages from other users, edit the same documents as others, or mention the same or similar topics. The resulting networks can be analyzed to reveal basic insights into an organization’s structure and dynamics. The creation and analysis of sample social media network datasets is described to illustrate types of enterprise networks and considerations for their analysis.

Abstract: Community based Question and Answer systems have been promoted as web 2.0 solutions to the problem of finding expert knowledge. This promise depends on systems’ capacity to attract and sustain experts capable of offering high quality, factual answers. Content analysis of dedicated contributors’ messages in the Live QnA system found: (1) few contributors who focused on providing technical answers (2) a preponderance of attention paid to opinion and discussion, especially in non-technical threads. This paucity of experts raises an important general question: how do the social affordances of a site alter the ecology of roles found there? Using insights from recent research in online community, we generate a series of expectations about how social affordances are likely to alter the role ecology of online systems.

The 2009 American Sociological Association Annual Meeting was held in San Francisco, California, August 8-11.

The ASA attracts thousands of sociologists, a subsection of whom have  a passion for the study of the Internet and its many forms of social impacts and uses.  The Communications and Information Technology Section of the American Sociological Association (CITASA) is the group that gathers many forms of social science research on the creation and uses of information technology.  This year’s meeting included two CITASA panels, round tables, a business meeting with awards, and a (windy!) boat ride through San Francisco Bay and beneath the Golden Gate Bridge.

The CITASA sponsored papers at the conference are listed below.  The range of work illustrates the continued interest in social science studies of the impacts of information technology.

Session 1: Cultural Divergences and Convergences: Mediated Communication, Community, and Social Networks
Presider: Laura Robinson, Santa Clara University
10:30 – 12:10 PM, Location: Parc 55 Hotel

Reframing Public Space Through Digital Mobilization: The Case of Flash Mobs
Virág Molnár

The Consumption of Online News at Work
Pablo J. Boczkowski

French Software Politics: The Freedom Discourse and Globalization from Below
Sara Schoonmaker

Convergence in E-Campaigning: Comparing the Use of Attacks on German and American Political Web Sites
Eva Johanna Schweitzer

The Flow of Mediated Culture: Trends of Supply and Demand 1960-2005
W. Russell Neuman

Session 2: Sociology of Communications and IT
Presider: Keith N. Hampton, University of Pennsylvania
2:30 – 4:10 PM, Location: Parc 55 Hotel

Gauging the Impact of e-Research in the Social Sciences
Ralph Schroeder, Eric T. Meyer, University of Oxford

Does Previous Technology Use Influence Later Opinions Linking Use to Work Experiences, Relationships, and Time?
Noelle A. Chesley, University of Wisconsin-Milwaukee

Medical Conversations in Technology Enabled Communities: Perceived Benefits of Participation in Online Patient Communities
Gul Seckin

Model Failure: Assemblages, Performances, and Uneasy Collaborations in Commercial Construction
Gina Neff, University of Washington; Brittany Fiore-Silfvast, University of Washington; Carrie Sturts Dossick, University of Washington

Core Discussion Networks, Internet and Mobile Phone Use: New Media are not Increasing Privatism in America
Keith N. Hampton, University of Pennsylvania; Lauren Sessions, University of Pennsylvania; Eun Ja Her, University of Pennsylvania

————–

Round tables (1-hour): Open Submission
Organizer: Steven G. Hoffman, SUNY Buffalo
8:30 – 9:30 AM, Location: Parc 55 Hotel

Table 1: Communication and Information Technologies at the Producer-Consumer Nexus
Presider: Chris Rhomberg, Fordham University

A Tale of Two Newspaper Chains: Gannett, Knight Ridder, and the Crisis of American Newspapers
Chris Rhomberg, Fordham University

Analyzing design activity in architect and user talk-in-interaction: a preliminary analysis
Rachael Luck, University of Reading

Distributed Cognition and the Emerging Peer-to-Peer Production Model
Michael Restivo, Stony Brook University

Table 2: Communication and Information Technologies, Community, and Sexuality

Negotiating identities/remediating queering desires: Coming out and coming of age online
Mary L. Gray, Indiana University

Sociological Examination of People’s Attitude Towards Online Dating
Xue Liu, Clemson University

The Amorous Migrant: Race, Interracial Desire and Relocation in Cyberspace
Nicholas Andrew Boston, Cambridge University/Lehman College of the City University of New York

Virtually Gay: The Internet and the Construction of Hybrid Queer Identities and Transnational Gay Cultures

James Paul Thing, University of Southern California

Table 3: The Internet and Political Engagement I
Presider: Phil Howard, University of Washington

Bringing the body back in: considering the role of the body in deliberative democracy
Amy Stuart, New School for Social Research ICANN and Internet Governance
Hangwoo Lee, Chungbuk National University

Social Capital and Political Mobilization in Online Community
Jun Young Ah, Yonsei university; Jeong-han Kang, Yonsei University

Vitalizing Donation Culture in Korea: Comparison of Strategies between Traditional Off-line and New Online Donations
Sun Hyoung Lee, Yonsei University

Table 4: The Internet and Political Engagement II

A Facilitator of Civic Engagement in Online Group Contexts
Ja Hyouk Koo, University of Virginia

Muslim resistance online: a diasporic Pakistani punk music subculture on the Internet
Dhiraj Murthy, Bowdoin College

Transylvania, the Internet, and the Strategic Construction of Ethnic/National Identity
J. Patrick Williams, Nanyang Technological University

Table 5: New Technologies, Social Change and Knowledge
Presider: James A. Evans, University of Chicago

How the Internet Shrinks Knowledge By Extending It
James A. Evans, University of Chicago

Socializing the Mobile Phone: Young Urban Poor’s Fascination at Play
Lip Soon Wong, Telenor Research and Innovation Center

Social Networks inside the Enterprise: analyzing colleague link patterns in an IT workplace
Marc Smith

Table 6: Computer Mediated Communication, Social Capital, and Symbolic Interaction
Presider: Simon Gottschalk, University of Nevada- Las Vegas

Dramaturgy, Technology and Public Health: Finding Sex Partners Online among Men who have Sex with Men
Anthony P. Lombardo, University of Toronto

eIntelligence: Social Intelligence in Computer-Mediated Communication
Simon Gottschalk, University of Nevada- Las Vegas

How to Ground a Child in Cyberspace: Parents’ Exploration of Norms and Rules
Mito Akiyoshi, Senshu University

Structured Talk and Web 2.0: Blogs and Community Formation

Kenneth M. Kambara, California Lutheran University

Table 7: Communication Technologies and Network Formation
Presider: John P. Robinson, University of Maryland

Events and Attendees in Two Countries: Factors Influencing the Size and Composition of Online Social Groups
Ryan M. Acton, University of California, Irvine

From each according to media? Testing Wellman’s theory of networked individualism
Bernard Hogan, University of Toronto

The Composition of a Korean Immigrant Social Network
Sun Kyong Lee, Rutgers University

Table 8: Information Production, Coordination and Distribution

Order, Coordination and Uncertainty in Online Information Systems”
Judd Antin, University of California, Berkeley
Coye V. Cheshire, University of California, Berkeley

When High Commitment Hurts: Evidence from Wikipedia
Andreea Gorbatai, Harvard University

Wikipedia: Community or a social movement?
Piotr Konieczny, University of Pittsburgh

Dr. Karen Cook, Stanford University

Dr. Toshio Yamagishi, Hokkaido University

Dr. Jodi O’Brien, Seattle University

and myself.

Earlier in the year, in March 2009, many people gathered at De Neve Plaza at UCLA to remember Peter Kollock.

On Monday, September 14th, at 10:30 PST, I met on the phone with Jerry Michalski and other participants for one of his regular weekly Yi-tan conference calls to discuss the scholarship and personal legacy of Peter Kollock.  In this podcast, I spoke about my years of working with Peter to apply the models of collective action dilemmas, common pool resources, cooperation, and conflict to the creation of Internet social institutions and resources. I discussed Peter’s work with his colleague Jodi O’Brien, and the lasting impact of his research into studies of cooperation, collective action, and common goods as well as his impact on students, business, and the field of social science.  Jodi and Peter were colleagues and co-authors for many years, creating a widely used text The Production of Reality: Essays and Readings in Social Psychology as well as several other publications.  I worked with Peter while studying for my Ph.D. at UCLA in Sociology.  Peter and I wrote about the sociology of the Internet together and co-edited a book Communities in Cyberspace.

I will post a few links to some of Peter’s work that has had the most impact on me in upcoming posts.

Listen here!

On August 5th and 6th I led a workshop with Martha Russell on social network analysis of social media as part of the Stanford Media X Summer Institute on New Metrics for New Media: Analytics for Social Media and Virtual Worlds this Summer.

New Metrics for New Media:  Analytics for Social Media and Virtual Worlds
Organizers:  Martha Russell, Marc Smith
August 5-6

Social media and virtual worlds offer two important frontiers for measuring earned engagement. In both, audiences are actively engaged as participants. This workshop covered foundational concepts in media measurement, describe new frontiers in measuring audience engagement in social media and virtual worlds, and provided hands-on experience in using new analytical tools.

This session also provided a walk through the basic operation of NodeXL, including generation of social networks from social media data sources like personal e-mail (drawing data from the Windows Desktop Search engine) and the Twitter social network micro-blogging system. Arbitrary edge lists (anything that can be pasted into Excel) can be visualized and analyzed in NodeXL. Attendees were encouraged to bring an edge list of interest. Sample data sets were provided.

Video about the New Media for New Metrics Workshop

Agenda WEDNESDAY, August 5: #124 Wallenberg Hall

08.30 – 09.00 – Welcome, Introductions & Overview

Martha Russell, Director, Media-X, Stanford University
Marc Smith, Internet Sociologist, Connected Action Consulting

Martha spoke about the changing media landscape and the need to measure new media in the mix of media consumption measurements.  She suggests that old measures may not always fit new media and that new media offer new opportunities (and challenges) for measurement.  Martha runs the Media-X program at Stanford that ran the workshop.

09.00 – 10.00 – The MEdia Generation (and others): Trends in Media Multitasking in the US and China

Don E. Schultz, Northwestern University Agora-IMC, Inc, BigResearch

Professor Schultz teaches marketing and communication at Northwestern University.  He runs a series of surveys about media consumption in the US and in China.  The results show people consuming multiple media at the same time, but not in the same mix (China uses mobile more than desktop).

10.30 – 11:30 – Panel: Metrics for Management Decisions

Moderator: Martha Russell, Media X at Stanford University

Richard Rommel, Senior VP Emerging Business, Best Buy
Blaine Baggett, Executive Manager, Office of Communication and Education
Jet Propulsion Laboratory, NASA
Louis-David Mangin, GrinShot
Maury Giles, Founder, Pursuit

Richard Rommel (Best Buy) discussed their Twitter strategy and commercials.  Best Buy now features their employees as a service via twitter to all askers and features that in their TV commercials.

JPL in Pasadena is using the web to push the “science is cool” (and fundable) message to all who will listen.  When NASA lands something on Mars its the JPL that gets the huge traffic spike.  They are opening up conversations between researchers and the public and increasing the surface area for science media.

11:30 – 12:00 – Synergies Between Search and Social Metrics

David A. Shamma, Yahoo Research

Ayman spoke about measuring engagement in a Yahoo Messenger video sharing and chat feature.  He found that chat activity spikes during the credits.  Except in Japan where strings of “wwwwwwwwwwwww” denote laughter and continue through out a shared video session.

Ayman also did a study of Twitter usage during last year’s political debates.  He found the chat did not echo the terms used in the debate.  Chat activity also spiked after the debate.

13:00 – 14:00 – Measuring Social Media & Digital WOM

Michael Wu, Chief Scientist, Lithium

Michael presented the ingredients of the Lithium Community Health Index which (pictured) includes the notion of interactivity (speed to reply).

14:00 – 15:00 – Panel: Metrics for Moderating and Managing Communities

Moderator: Marc Smith, Internet Sociologist, Connected Action Consulting

Sameer Patel, Pretzel Logic
Gail Ann Williams, Director of Communities, Salon.com

Gail and Sameer focused on the ways they directly manage communities in a commercial environment.  Gail tuns the community for Salon’s Table Top and Well social spaces.  She runs fee based spaces that require customer retention (but sometimes wish they could selectively apply customer ejection!).

Sameer consults on the selection and use of enterprise social media.  He focused on the metrics his customers want to see in an internal deployment, focused on usage and connection rates among users.

15:15 – 17:00 – Social Network Analysis Tutorial: Part 1
Concepts, Data, & Tools for Producing the Social Graph

Marc Smith, Internet Sociologist, Connected Action Consulting

I showed the slides previously presented at the Catalyst Conference, this time in a longer form at the workshop: http://www.slideshare.net/Marc_A_Smith/2009-catalyst-conference-social-media-research slides focuse on the use of social network and activity analysis to identify key contributors in social media spaces.

17.00 – 17.30 – Discussion: Navigating the Terms in Social Media Metrics

Moderator: Don Schultz

———————————————–
Agenda THURSDAY, August 6: #124 Wallenberg Hall

08.30 – 09.30 – Patterns of Mobile Media Advertising–Taking 3G Portals as an Example

Chunlin Duan, South China University of Technology Profit

Professor Duan is Dean of the School of Communication at South China University of Technology.  She described mobile device usage in China (huge!) and the major focus on IM, chat, and message boards, along with the dominance of QQ and its virtual goods and currency.

09.00 – 09:30 – Cell Phone Usage in Japan for Social Networking and Advertising

Neil Rubens, University of Electro-Communications, Tokyo

When toddlers have cell phones mobile device penetration is high!  Japan is highly mobile (although on its own island of technology) and using social network services via their devices.

09:30 – 10.30 – Panel: Emerging Metrics for Virtual Worlds & Immersive Environments

Moderator: Greg Nuyens, Qwaq Forums
Gene Yoon, Former CFO, Second Life
Parvati Dev, Innovations in Learning
Henrik Bennetsen, Stanford Humanities Lab

Gene Yoon is the former CFO of Second Life, he spoke about the use of metrics in his business.  He focused on engagement as the behavior he wanted to measure.

Parvarti Dev is a former Stanford Professor using virtual reality social worlds to role play emergency room procedures among nurses and doctors.

11:00 – 12:00 – Trends in Metrics for Self Monitoring

Gary Wolf, Sr. Editor, WIRED

Gary is documenting the emerging world of the “Quantified Self” – people using mobile devices with sensors and networks to collect detailed records of their own behavior and medical vital statistics.  These data sets, in aggregate, may reveal new insights for evidence based medicine, self improvement, and public safety.

13:00 – 15:30 – Social Network Analysis Tutorial: Part 2

Concepts, Data, & Tools for Filtering and Decorating the Social Graph

Marc Smith, Internet Sociologist, Connected Action Consulting

A step-by-step guide to creating, analyzing, and visualizing a social network.

16:00 – 16:30 – Discussion and WrapUp: Gleaning Meaning from Measurements

Moderator: Martha Russell

Share and Enjoy:

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