A new paper on visualizing social media has been released on the University of Maryland, Human Computer Interaction Laboratory tech report archive.  Co-authored by Derek Hansen,  myself, and Ben Shneiderman, the paper describes and visualizes the patterns of connections formed when people tweet about events like conferences and news stories.

EventGraphs_2010_HCIL_Tech_Report

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

Hansen, D., Smith, M., Shneiderman, B.
EventGraphs: Charting Collections of Conference Connections
HCIL-2010-13

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

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

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

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

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

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

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

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

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

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

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

On May 21st, I will speak at the Social Graph Symposium in Mountain View, California along with Sharad Goel – Research Scientist at Yahoo and DJ Patil – Chief Scientist at LinkedIn.

The Social Graph Symposium brings together academics, experts, influencers, executives, and other prominent figures with an interest in social graphs. The event is centered around the social graph and its implications for business, technology, and the community.

Discussions this year will center around:

• State of the Social Graph
• Latest Social Graph Research
• Activism
• Advertising
• Influencer and Word-of-Mouth Marketing
• Marketing
• Retail
• Search
• Social Gaming

Here is a map of recent connections among  twitter users who mention the term “Social Graph”:

In this video interview, John and I summarize the themes discussed at the symposium including the political implications of inequality of technology access and the literacy to use it.  John describes his efforts to map the global blogosphere and I describe the ways social media creates social networks that can be extracted and mapped.  What does it take to be a communicator in a digital media environment?  We discuss the privacy rights of public data and the use of data in ethical ways.  Not everyone with a fiber-optic cable and server room operates under ethical guidelines.  Given that digital communication is inherently traceable communication, could it be that not everyone should take the risks of communicating?    Digital communication makes messages more findable and available which is a virtue when you want your message heard widely.  It is getting harder to limit distribution of content to select audiences.  I like to argue that the destiny of all information is to be made public if only because information never becomes less public.

IE University has a YouTube channel with lots of interesting  video (in English and Spanish) related to communication, innovation, and social media.

In March, 2010 I had the pleasure of attending and speaking in Segovia, Spain at the IE University Department of Communication at the symposium on Transnational connections: Challenges and opportunities for communication.

The symposium is organized with Center for Global Communication Studies at the Annenberg School for Communication, University of Pennsylvania.  I participated in a video interview with several attendees that was created to summarize the themes and topics discussed at the symposium.  The video includes my colleague John Kelly, from the Berkman Center for Internet & Society, Harvard University and Founder of Morningside Analytics, along with Adrian Monck (World Economic Forum), and Shanto Iyengar (Stanford University).

In a shorter segment several people were asked “What makes a good modern communicator”: here is my reply.

Here was John Kelly’s response as well.

More video is available from IE University’s YouTube channel.

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

Hands-on Workshop – Social Media Network Analysis in NodeXL

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

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

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

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:

Some photos taken during the trip are available after the jump:

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?

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

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.

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.

View Larger Map

Other Maps:
Yahoo | Mapquest | Microsoft

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.

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

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

A new book E-Research: Transformation in Scholarly Practice edited by Nicholas W. Jankowski on the ways social science research is being changed by the rise of social media has just been released by Routledge.  My colleagues and I contributed a chapter on the ways that information visualization of social media is a useful technique to identify research questions and discover answers about the nature of human association when mediated by computation.  The volume contains work from an all-star line-up of researchers who address the opportunities and challenges of performing research with computer-mediated data about social life.

The blurb about the book describes it as:

“No less than a revolutionary transformation of the research enterprise is underway. This transformation extends beyond the natural sciences, where ‘e-research’ has become the modus operandi, and is penetrating the social sciences and humanities, sometimes with differences in accent and label. Many suggest that the very essence of scholarship in these areas is changing. The everyday procedures and practices of traditional forms of scholarship are affected by these and other features of e-research. This volume, which features renowned scholars from across the globe who are active in the social sciences and humanities, provides critical reflection on the overall emergence of e-research, particularly on its adoption and adaptation by the social sciences and humanities.”

Our chapter is “A Picture is Worth a Thousand Questions: Visualization Techniques for Social Science Discovery in Computational Spaces”, co-authored by  Howard T. Welser, Thomas Lento, Marc Smith, Eric Gleave and Itai Himelboim.  In it, we describe the ways that using information visualizations of social media data sets is a useful way of discovering insights, patterns, and clusters.  We illustrate the paper with several examples of social media information visualizations that display the range of behavior among contributors to social media spaces.

Here is the table of contents for the volume:
Introduction
1. The Context and Challenges of e-Research Nicholas W. Jankowski.

Conceptualization
2. Towards a Sociology of e-Research: Shaping Practice and Advancing Knowledge Ralph Schroeder and Jenny Fry
3. e-Research as Intervention Anne Beaulieu and Paul Wouters.

Development
4. Developing the UK-based e-Social Science Research Program Peter Halfpenny, Rob Procter, Yu-Wei Lin and Alex Voss
5. e-Research and Scholarly Community in the Humanities Paul Genoni, Helen Merrick and Michele Willson
6. The Rise of e-Science in Asia: Dreams and Realities for Social Science Research. Case Studies of Singapore and South Korea Carol Soon and Han Woo Park.

Collaboration
7. Creating Shared Understanding across Distance: Distance Collaboration across Cultures In R&D Petra Sonderegger
8. Moving from Small Science to Big Science: Social and Organizational Impediments to Large Scale Data Sharing Eric T. Meyer.

Visualization
9. Visualization in e-Social Science Mike Thelwall
10. A Picture is Worth a Thousand Questions: Visualization Techniques for Social Science Discovery in Computational Spaces Howard T. Welser, Thomas Lento, Marc Smith, Eric Gleave and Itai Himelboim.

Data Preservation and Reuse
11. Web Archiving as e-Research Steven M. Schneider, Kirsten A. Foot and Paul Wouters
12. The Promise of Data in e-Research: Many Challenges, Multiple Solutions, Diverse Outcomes Ann Zimmerman, Nathan Bos, Judy S. Olson and Gary M. Olson
13. Naming, Documenting and Contributing to e-Science Samuelle Carlson and Ben Anderson.

Access and Intellectual Property
14. Open Access to e-Research Robert Lucas and John Willinsky
15. Intellectual Property in the Context of e-Science Dan L. Burk.

Case Studies
16. Situated Innovations in e-Social Science Bridgette Wessels and Max Craglia
17. Wikipedia as Distributed Knowledge Laboratory: The Case of Neoliberalism Clifford Tatum and Michele LaFrance

My colleagues Derek Hansen and Ben Shneiderman (University of Maryland) and I have just finished the second version of our tutorial/manual for the NodeXL social network analysis toolkit for Excel.

The latest version of the tutorial Analyzing Social Media Networks: Learning by Doing with NodeXL is now available from the University of Maryland Center for the Advanced Study of Communities and Information (CASCI) web site.  We will use this version of the document in our upcoming tutorial at the Communities and Technologies conference at Penn State University on June 24th.

We plan to continue to expand the tutorial to include a step-by-step guide to the analysis of several major social media sites like Twitter, Facebook, Wikipedia, YouTube, delicious, and flickr as well as personal stores of social media like your own email (if it is stored in a Windows Search Index found on most Windows desktops).  Our goal is to create an easy-to-follow guide to network theory for people who new to the field or who do not want to develop programming skills to perform network analysis.  We are focused on social media as a data source for social media although other examples are included,  like the United States Senate voting network that reveals interesting patterns in the connections created when votes are cast.  Using 2007 data it reveals which Senators are most likely to change party affiliation.

Your comments, corrections, and suggestions for improving the document are welcome.

Instructors interested in teaching classes about social networks are welcome to make use of both the NodeXL toolkit and the document to guide students through the core concepts of social network theory.

Here is the table of contents:

1) Basic: Getting started with NodeXL

Data entry
Showing the graph
Highlighting an edge
Importing an edge list

2) Layout: Arranging Vertices in the Graph Pane

Automatic Layout
Directed Graph Type
Updating the Graph Pane
Manual Layout
Preserving manual layout
Zooming and Scale

3) Visual Design: Making network displays meaningful

Vertex Colors
Adding Descriptive Data
Changing Vertex Size (and other properties)
AutoFilling Columns
Legend
Changing General Graph Appearance

4) Labeling: adding text labels to vertices and links

Adding Primary Labels
Adding Secondary Labels
Adding Tooltips

5) Graph Metrics: Calculating and visualizing metrics

Computing Graph Metrics
Saving a NodeXL File
Kite Network Example
Opening an existing NodeXL File
Overall Metrics
Vertex Metrics
Degree
Betweenness Centrality
Closeness Centrality
Eigenvector Centrality
Clustering Coefficient

6) Preparing Data: Merging Edges and Sorting to Label Data

SeriousEats Analysis
Merging Duplicate Edges
Sorting Data
Auto-Filling Data Columns
Formulas

7) Filtering: Reducing clutter to reveal important features

Dynamic Filters
Filtering by Autofilling the Visibility Column
Subgraph Images
Putting It All Together

Clustering: Identifying and displaying vertex clusters

2007 Senate Voting Analysis
Creating Clusters Manually
Changing Advanced Layout Options
Creating Clusters Automatically
Showing and Hiding Clusters

I just read a new paper from Jennifer Preece and Ben Shneiderman that provides a nice framework for the ways people contribute at different rates to collective projects in general and social media on the Internet in particular.

Preece, Jennifer and Shneiderman, Ben (2009).  The Reader-to-Leader Framework: Motivating Technology-Mediated Social Participation, AIS Transactions on Human-Computer Interaction (1) 1, pp. 13-32.

Available at: http://aisel.aisnet.org/thci/vol1/iss1/5, the paper is published in a new journal, Association for Information Systems  Transactions on Human-Computer Interaction, and is likely to be of interest to those in the social media and network analysis community. The main argument is that there are distinctive activities that people move through: initially as readers, then contributors (in small then larger ways), then collaborating with others to make larger contributions, and then to leadership (policy making, enforcement, coping with disruptions, mentoring novices, etc.).  The figure (above) from the paper is modeled on Wikipedia where these activities have been studied extensively, but they argue that these activities can be found in many technology-mediated social media.  The conversion rate from one activity to another is often as low as 1 percent (for example, there are half-a -billion readers of wikipedia, but just 1600 admins who are effectively the leaders), so the paper offers suggestions for improving the usability and sociability design to raise the conversion rate.

Peter Pirolli, research at PARC, presented the keynote about the challenges and opportunities for the use of social media to address social problems.

There is growing interest in this space, for example NSF funding was significantly increased this year. For example, there is the new NSF Social-Computational Systems (SoCS) program: http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503406&org=NSF&sel_org=NSF&from=fund

President Obama’s recent speech to the National Academy of Science (http://www.nasonline.org/site/PageServer) sets some of the context for this group’s vision, he speaks about crowd sourcing and its impact on science. (See minute 28:30) video is on the web site of the National Academy of Science http://edg1.vcall.com/video/nas/launch.asp

“I have charged the White House Office of Science and Technology Policy with leading a new effort to ensure that federal policies are based on the best and most unbiased scientific information. I want to be sure that facts are driving scientific decisions – and not the other way around.

As part of this effort, we’ve already launched a website that allows individuals to not only make recommendations to achieve this goal, but to collaborate on those recommendations; it is a small step, but one that is creating a more transparent, participatory and democratic government.”

Elsewhere in the speech there is a refernce to the role of (appropriately) digitizing medical records which I think includes the idea that people will increasingly gather online to work towards better personal health:

“The Recovery Act will support the long overdue step of computerizing America’s medical records, to reduce the duplication, waste, and errors that cost billions of dollars and thousands of lives.

But it’s important to note: these records also hold the potential of offering patients the chance to be more active participants in prevention and treatment. We must maintain patient control over these records and respect their privacy. At the same time, however, we have the opportunity to offer billions and billions of anonymous data points to medical researchers who may find in this information evidence that can help us better understand disease.”

There seems to be a role for the kinds of online communities and social media that people turn to when facing physical or medical challenges.

National Initiative for Social Participation at University of Maryland
http://iparticipate.wikispaces.com/Motivating+Scenario
http://chronicle.com/daily/2009/05/17280n.htm
http://www.flickr.com/photos/marc_smith/sets/72157617551332795/

[Vladimir Barash is liveblogging the ICWSM conference]

10.30am A categorical model for discovering latent structure in social annotations (Said Kashoob)
Given a collection of web objects, users and tags, can we model the underlying tag generation process?

-Discover implict communities of interest?

-Categories of related tags?

-For given category, id most relevant objs for category

-compare categories

Initial thoughts: content-based topic modeling (Latent Dirichlet Allocation, LSA). Recent work applying LDA models to tags (Wu 2006, Zhou 2008)

Modeling social annotations: the process that generates content is fundamentally different from the annotation process (many authors per “document” = tag collection, not aware of each other)

Community based categorical annotation model (CCA). Communities are groups forming around interests, etc. Each community has a number of categories as its world-view. For each object, a community draws tags from the appropriate underlying categories

Object annotations are generated by communities. Each community selects tags from its category set.

-use Gibbs sampling to recover a joint distribution of tags, categories and communities

-can do inference to find most likely tags per category, per community

Content-based topics vs. tag-based categories

Exploring content vs. annotation: for pairs of objects that are similar in category space, how topically similar are they? Reslt: objects with similar content do not necessarily have similar tags and vice versa

Rubix cube example: objects similar both in category and topic are solutions to the puzzle, objects that are only similar in category are puzzles / games, objects that are only similar in topic are math pages

11am Content-based summarization and categorization in the blogosphere (Ahmed Hassan)

How can we decide which blogs are more important / influential? Given a set of blogs related to a particular topic, find a subset of blog feeds to read that have continued interest in the topic.

Can we use hyperlink popularity based algorithms for speeches and blogs? Yes, but they might not work very well

Use textual similarity to link posts instead of hyperlinks: maybe blog A affects blog B? Given a set of blogs, build a graph where nodes represent posts/feeds and edges link posts/feed with simliar text

Use a pagerank-like measure to calculate importance score of a blog in the similarity network

How can we select nodes that are important but diverse? Add discounting factor based on similarity of node to neighbors

dataset: TREC blog datase

Evaluation: use linear threshold diffusion model! How many blogs covered (activated) by first k blogs in rank. Also split data by time to see how valid is rank(t) for predicting coverage at t+1. Approach also does a little better at precision-at-k on the TREC blog dataset

11.30am Supervised ranking of syntactic configurations for finding targets of semantic expressions (Jason Kessler)

Trying to find targets of sentiment phrases (“while the dealership was friendly, the *car* was a disappointment”)

Sentiment expressions only link to physical targets (“Bill likes to drive the car”)

Two-domain corpus: cars and camera

Baselines – proximity, one-hop with dependency parser

Approach – learn to target from a corpus. Supervised ranking instead of classification. Uses linear-kernel RankSVM, off-the-shelf approach

Results – supervised ranking does better than proximity, one-hop, approaches interannotator agreement

Future work: inter-sentential target

3pm Stochastic models of user-contributory web sites (Tad Hogg)

Focus: describe aggregate group behavior

-determines structure and usefulness of user-participatory sites

Models enable:

-predicting user behaviors

-incentivizing user participation

Stochastic modeling summary:

-Start with individual user behavior, specify states and transitions between states

-Determine collective behavior (details in paper)

Illustration: Stochastic Model of Digg

-Phenomenology: users submit and vote on news stories, Digg promotes popular stories to front page, allows social networking (friends, fans)

Model of Digg voting behavior: visibility and interestingness -> votes. Extension to prior model (Lerman ’07).

- “law of surfing” for viewing web pages (Huberman et al. 98)

- incremental average growth in number of voters’ fans

- construct equation for dynamics of vote volume for a story from state diagram that formalizes visibility and interestingness. Params for vis and interest estimated from story sample. Estimate viewers watching stories from models and data.

Data: front page and upcoming stories since May 06

Modeling story visibility: story location, navigating web sites, number of fans. Each voter enables fans to see story via friends interface.

Modeling story interestingness: topic, novelty, popularity. Can estimate from web-based experiments, e.g. Salganik et al. 06, but can estimate from models and data.

Results: model captures qualitative features – slow growth initially, influence of fans on promotion, rapid growth if story promoted (much more visible to users)

Results: the number of fans have not yet seen the story drops, number of votes on story grows significantly after story gets promoted. “Promotion line” in number of fans / interestingness splits stories into will be / won’t be promoted with 95% accuracy

Predictions from early behavior: can predict #votes from first 4 votes (similar to results for YouTube), but “law of surfing” and incremental growth important parts of model

Conclusions: stochastic process approach connects user and system behaviors, applicable to social media in general when users have limited information and actions, limited use of personalized history.

3.30pm Personal information management vs resource sharing: towards a model of information behavior in social tagging systems (Markus Heckner)

Why do people tag?

Tagging: a fourth layer of indexing? (On top of author keywords, intellectual indexing by information professionals, and auto-tagging)

media type influences tagging: differences in number, language, function of tags btw Connotea, Flickr, YouTube, Delicious

Method: Scientific crowdsourcing using Mechanical Turk

Assumption: Different motivations for taggs

-Organization of one’s own digital content, i.e. personal informational management (Delicious, Connotea), vs. information sharing (Flickr, YouTube)

Questionnaire Design: Question Types

-online questionnaire posted as “human intelligence task.” asks general information, general motivation, tagging motivation and understanding, social bookmarking and search, recent usage.

Data: ~150 subjects, users of Flickr, YouTube, Delicious, Connotea

Results:

Motivation: YouTube is significantly weaker-motivated for PIM, Delicious much weaker for sharing, Flickr and Connotea about even

Perception of tagging: Connotea users perceive tagging as most easy, follow by YouTube, Delicious, Flickr (not significant). Connotea user agree very strongly that tagging is a useful feature

Towards a model of tagging behavior: Shneiderman’s approach towards social software (social spheres), etc.

4pm Motivational, structural and tenure factors that impact online community photo sharing (Oded Nov)

Why do people in online communities share? Can we quantify the drivers for sharing (or not sharing) and their effect on actual behavior?

Three types of questions as framework:

Why – drivers of sharing (Motivation, structural properties, personality, privacy concerns)

What – type of information shared (code, content/facts, meta-info (tags), photos)

Where – context of sharing (OSS, Wikipedia, Flickr)

Creation vs. sharing: the act of sharing is separate from the act of creation

People take photos regardless of the sharing act (really?), the “second act” of sharing photos is optional, separate from the “first act” of photo sharing

Identifying the factors in sharing: motivational (extrinsic vs. intrinsic), structural factors (position of user in community network), tenure in community

Motivations: enjoyment (self/intrinsic), commitment to the community (others/intrinsic), self-development (self/extrinsic), reputation (others/extrinsic)

Response variable: artifact sharing per tenure year, IV: motivational vars + structural (number of contacts) + tenure (years since started sharing)

Method: combine user-reported (survey) data and system data: what people say + what people do. N=278, used only “pro” users (>200 photos) with at least 3 months’ tenure on Flickr.

Results: significant positive effect of commitment, negative of self-development, positive of number of contacts, negative of tenure, rest not significant.

Why is enjoyment not correlated with sharing? Users may be motivated more by “fun” of creation rather than content sharing.

Why is correlation between self-development and photo sharing nefative? A tradeoff between contribution quality and quantity? Greater self-development motivation -> focus on the quality of artifacts shared, at the expense of quantity

Summary:

quality / quantity tradeoff, fun is not an issue?, diminishing sharing

4.30pm Modeling Blog Dynamics (Michaela Goetz)

Blogosphere is a system of interactions: Entities: Bloggers, Posts, Topics

Model: simple set of rules (followed by blogger) that creates these interactions

Evaluation: creating a synthetic blogosphere, comparing it to real blogosphere

Motivation: forecasting, advertising

How is this different from modeling social network? 2 networks combined: Blog vs. Post network, complex temporal dynamics

goal: model micro-level interactions to observe macro-level interactions in blogosphere

Properties of the blogosphere:

-Topological – blog, post = follow power law distribution

-Temporal – user posting activity, popularity over time (link creation)

Burstiness (Slope = 1 of aggregation level vs. entropy) & Self-similarity (Linearity of aggregation level vs. entropy)

Inter-posting time follows a power law

Time t vs. number of in-links t days after publishing follows a power law

Desired model: simple (no parameters), intuitive (local rules), creates realistic topology and dynamics

First-try solution:

-inter posting times sampled from exponential distribution, links created using pref attachment – leads to exponential inter-posting distribution, poisson degree distribution (really?), etc.

Second-try solution (Zero-Cost):

In every round, for every blog, user u takes a random walk step, if he reaches 0, he decides to post P

when he posts, he can make a link or not, if he makes a link, he can choose a neighbor based on frequency of links or non-neighbor, then chooses some post of neighbor and links to random posts upward in the cascade

This model accurately reproduces both the topological and temporal patterns (at a qualitative level – same distributions, different though relatively close exponents. Biggest difference: 1.5(sim) vs. 0.7(real) in inter-posting time exponent)

On Tuesday night Marc Smith and I will be presenting the poster for our paper, “Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context” at the International Conference for Weblogs and Social Media. You can find the poster, co-authored with Marc Smith (Telligent Systems), Lise Getoor (University of Maryland) and Howard T. Welser (Ohio University), here. The full text of the paper has more information, but the poster is a good summary of the key concepts in the paper:

• What roles do people play in social media?
• What contexts shape user behavior in social media?
• How can we leverage roles and context together to predict future user behavior (in terms of contribution type) from past user behavior?

The specific research question being addressed is: can we predict whether a particular contribution to Live Q&A (a Microsoft-sponsored community question answering site) will be contain factual information, or discussion / chat. It is possible to do the prediction based on the text of the contribution, but such an approach focuses entirely on the content, and not on the actor – the user who is making the contribution. If we leverage actor-centric information (what role does he/she play in the community: an “answer person” or a “discussion person”? is he making the contribution in a discussion-oriented context, such as implied by tagging the contribution as “fun,” or a fact-oriented context, such as implied by tagging the contribution as “math”?), we find we can build a decent predictor at very low cost with very few variables. If we use just role information or just context information, we do reasonably well… but if we use both, we do *much* better. While the question we’re answering here is quite specific, the advantage of our approach is that it can be applied to almost any social media context – any place online where users can both contribute content and interact with others. We could just as easily create a predictor for contributions to Yahoo! Answers, or even to Wikipedia (if we had the relevant data). This is definitely food for thought / opportunity for future work

Princeton Center for Information Technology Policy

Here is the slide deck I presented at the “Studying Society In A Digital World” conference at the Center for Information Technology Policy at Princeton University.  They just posted most of the conference slides.  I took some pictures and posted to flickr.  Here is the slide deck I presented along with my talk at the conference.

The theme of mobile sensor data shared via social networks was one reinforced by the many presentations at the conference. In my talk, I focused on the idea that information want not to be free or expensive, rather, information wants to be copied.  Like DNA, the goal of any string of bits is to make a duplicate copy of themselves.  Several technical realities mean that while information may exist on a spectrum from private to public, it only moves in one direction (public) and almost never back.  Once made public on the Internet, even if only for a moment, a photo, document, or other digital object is almost certainly to have been copied, indexed, backed up, or replicated.  All efforts to delete a digital object once widely distributed is like trying to take wine out of water.  This is because all cryptography become brittle over time, most bits end up exposed after they get distributed, and more events trigger widespread distribution of bits than expected (for example, linking a photo, and a location, to a tweet that gets copied to LinkedIn and Facebook, that then appears in an RSS feed and is copied from there to Friend Feed.  As it travels, information looses more of the access controls that initially made it relatively private until it is effectively public.

We got some great questions from the attendees about how they can apply these approaches to their social media investments.  Tools like NodeXL can be helpful for people interested in structural data analysis.  Kate has built tools for performing semantic analysis of social media content over time.

The video is now available on vimeo:

Beyond Buzz: On Measuring a Conversation (Web 2.0 Expo 2009 – Katie Niederhoffer & Marc Smith) from Steffan Antonas on Vimeo.