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?

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

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.

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.

Share and Enjoy:

]]> http://www.connectedaction.net/2009/12/15/book-communities-in-cyberspace-ten-years-later/feed/ 4 http://www.connectedaction.net/2009/12/15/book-communities-in-cyberspace-ten-years-later/ http://feedproxy.google.com/~r/connectedaction/~3/YMW5uHU3Bb0/ 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!

—

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.