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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NodeXL Version 1.0.1.159

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

Previous HICSS papers:

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

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

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

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

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

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

Here are some sample event graphs:

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!

—

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

Here is the video:

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

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

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

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

JCMC Article: Discussion Catalysts

My co-authors Eric Gleave, from the University of Washington, Department of Sociology and Itai Himelboim, from the University of Georgia, Department of Communications, are pleased to note the publication of our paper “Discussion catalysts in online political discussions: Content importers and conversation starters“ in the Journal of Computer-Mediated Communication (JCMC) http://jcmc.indiana.edu/ at http://ping.fm/7NF5T

The paper describes the roles of “discussion cataylsts” who populate political web boards (newsgroups) and start the threads that get people talking!  It turns out that only a very few people in a community get to start many threads successfully.  Discussion catalysts have a knack for sparking conversations: setting the agenda for the community at large.  Discussion people have high “-in-degree”, they get replied to by lots of people, but low “out-degree”, they tend not to reply that much themselves.  The people whoreply to discussion catalysts, in contrast, do reply to one another densely.  These are the discussion people, a role  that will be the focus of a subsequent paper!

JCMC - Discussion Catalysts

Abstract:

This study addresses 3 research questions in the context of online political discussions: What is the distribution of successful topic starting practices, what characterizes the content of large thread-starting messages, and what is the source of that content? A 6-month analysis of almost 40,000 authors in 20 political Usenet newsgroups identified authors who received a disproportionate number of replies. We labeled these authors ‘‘discussion catalysts.’’ Content analysis revealed that 95 percent of discussion catalysts’ messages contained content imported from elsewhere on the web, about 2/3 from traditional news organizations. We conclude that the flow of information from the content creators to the readers and writers continues to be mediated by a few individuals who act as filters and amplifiers.

Previously, we published “Picturing Usenet” in the JCMC, a paper that features several images of information visualizations of threaded discussions and authors over time.  That paper was based on an early work with Fernanda Viegas (now at IBM Research, Cambridge, then as an MIT graduate student in the MediaLab interning with me at Microsoft Research in Redmond, Washington).
[2009 - JCMC- Discussion Catalysts - Himelboim, Gleave and Smith]

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.

The Communities and Technologies conference is holding its 4th meeting in Penn State June 24-27.  This conference gathers a range of scholars interested in online community, social media, social networks, and mobile social software.  A paper “Analyzing (Social Media) Networks with NodeXL” has been accepted for publication in the conference!  Congrats to my co-authors!

Paper Title: Analyzing (Social Media) Networks with NodeXL

Authors: Marc Smith (Telligent Systems), Ben Shneiderman (University of Maryland), Natasa Milic-Frayling (Microsoft Research), Eduarda Mendes Rodrigues (Microsoft Research), Vladimir Barash (Cornell University), Cody Dunne (University of Maryland), Tony Capone (Microsoft Research), Adam Perer (University of Maryland, now at IBM Research), Eric Gleave (University of Washington)

Abstract: In this paper we present NodeXL, an extendible toolkit for network data analysis and visualization, implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL features through analysis of a data sample drawn from an enterprise intranet social network, discussion, and wiki. Through a sequence of steps we show how NodeXL leverages and extends the broadly used spreadsheet paradigm to support common operations in network analysis. This ranges from data import to computation of network statistics and refinement of network visualization through a selection of ready-to-use sorting, filtering, and clustering functions. ]]> http://www.connectedaction.net/2009/06/08/2009-ct-nodexl-and-social-queries-a-social-media-network-analysis-tool-kit/feed/ 0 http://www.connectedaction.net/2009/05/19/icwsm-liveblog-day-2/?utm_source=rss&utm_medium=rss&utm_campaign=icwsm-liveblog-day-2 http://www.connectedaction.net/2009/05/19/icwsm-liveblog-day-2/#comments Tue, 19 May 2009 17:35:52 +0000 Vlad43210 http://www.connectedaction.net/?p=1158

[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)

[Vladimir Barash is liveblogging the ICWSM conference]
9-10AM: A Tempest: Or, on the Flood of Interest in Sentiment Analysis, Opinion Mining, and the Computational Treatment of Subjective Language (Lillian Lee)

-Sentiment analysis using discussion structure: clasify speeches in US congressional floor debates as supporting or opposing proposed legislation -Individual doc classifier -agreement (degree) classifier for pairs of speeches

-Agreement info allows COLLECTIVE CLASSIFICATION – “agreeing speeches should get the same label”

-ECON: debate about effect of sentiment on sales
-comScore (users willing to pay 20-99% more for 5 star item vs. 4 star item)
-Jury is still out

-SOC: What opinions are influential? (Niculescu-Danescu Muzyl et al.)
-Prior work has focused on features of text and has not been in context of sociological aspects of reviews
-look at helpfulness scores

-What about review’s star rating in relationship to others?

-theories from soc / social psych:
-conformity
-brilliant but cruel

-Are the social effects just textual correlates?

-would like to control for actual quality of review text. Manual annotation? Tedious, subjective. Automatic clasification? Need extremely high accuracy guarantees.

-use plagiarism (1% of all reviews) to control for text quality! findings hold for plagiarized pairs

Summarizing:

-Sentiment analysis has many important applications – to researchers, to citizens, to governments

-encompasses many interesting research questions

-extends to many areas

Stand-out question: matt hurst and the user as generative model for opinions

10.30 AM: Gesundheit! Modeling Contagion through Facebook News Feed
(Eric Sun, Itamar Rosenn, Cameron Marlow, Thomas Lento)

Motivation: how do ideas diffuse through a large social network?

-Theory of the Influentials (Gladwell)

-Accidental Influencers(Watts): anyone can be an “influencer.” Ideas don’t spread via influentials, ideas spread like viruses (susceptible or not), goal to find a large number of susceptible people

Q: are contagions triggered by small # of sources? What are some characteristics of diffusion chains on Facebook? Can we use demographic or behavioral characteristics to predict size of diffusion chains a particular user will create?

Spreading ideas on Facebook – through News Feed

-Page Fanning = becoming fan of people, orgs, etc.

-Mechanics: Alice fans a page, Bob sees Alice’s action on his News feed, Bob fans page as well (link: Alice -> Bob)

-Strong ties: links depend both on friendship and on actions (following)

-Median page has most of its fans in one (weakly) connected cluster

-Large clusters Not Started by “one guy” – roughly 15% of fans in the biggest cluster of each Page are start points

-Variability in this percentage becomes very small as #fans increases

-Clusters are formed when many short diffusion chains merge

-Data: actor to follower connections for ~300,000 FB paes

-Main dataset: page-level data

-Second dataset: select 10 random, representative pages (at least 40 days old had at least 5k fans) and analyze users that start chains

-Prediction Model: Response = max_chain_length, Predictors: gender, log age, log FB age, etc. Method: 0-inflated neg binomial regression

-results: Demographic characteristics not important, number of Facebook friends not important, feed exposure is the strongest predictor with coefficient ~ 1 (so a 1% increase in the number of people who see ego’s fanning ~ 1% increase in chain length)

-Comment: this is global focus, not local focus. What about the interpersonal dimension, i.e. the likelihood that Alice infects Bob?

-Comment: support for Duncan Watts’ idea

————————————————————————————————————————————————-

11am Seeking and Offering Expertise across Categories: A Sustainable Mechanism Works for Baidu Knows
(Jiang Yang, Xiao Wei)

Baidu Knows: Chinese QnA site

-Growing extremely fast: more than 80 million questions asked in 4 years.

-Huge user population (2.6 mln users). Knowledge repository as online source

-Points! Points! Points! (flexible amount of extra points set for best answerer, more points buy more answers, etc.)

-Building sense of community: honor title system (including traditional Chinese titles!), online chats, etc.

-Data: Full history of QnA 12/07-05/08. 9.3 mln questions asked, 5.2 mln (56%) resolve, 2.6 mln users participated

-3.3 answers for each question (vs. 7.3 for Yahoo! Answers, note that Yahoo! Answers encourages answering more than asking)

-Significant categorical difference in awarded points: low(brand, science, food) vs. high (medicine, computer, music)

-Price of answering positively correlated to popularity of category

-Order difference: according to human rating of sample questions, order of answers doesn’t matter, but first answer has highest chance to be best answer, more points awarded for later best answers

-Reinforcement cycle: encourage continuing

-Answerer performance positively correlated with activity level. More active answerers choose less expensive questions, questions with fewer answers. More active answerers working harder (longer answers), and more focused (on particular category)

Reinforcement cycle: choose less competitive q’s -> better performance -> more efforts -> more focused -> choose less competitive q’s

-Askers: learn how to better ask: more active askers, ask cheaper questions, experienced askers get more answers with per point they ask (slight trend).

-Asker/Answerer hybrids (22% of pop): core of contribution! Much more active (almost 1/2 total questions), more generous (offer higher award: 12.3 per question versus 11.6 on average in general, share same pattern as normal asker but paying higher each time), not necessarily experts, incentivized

Seeking and offering across categories: some categories are pretty self-contained, others are more porous. Lots of cross-category contribution

-A sustainable mechanism is working on Baidu Knows (that’s a good discussion question!)

11.30 AM: Community Structure and Information Flow in Usenet: Improving Analysis with a Thread Ownership Model (Mary McGlohon, Matthew Hurst)

-Compare communities of online social nets may lend insight into how groups form and thrive

-How does info diffuse between communities?

Data: Usenet, 200 politically-oriented newsgroups (bulletin boards) – polit in name, Jan 04 – june 08. several countries, 19.6 mln unique articles, 6.2 mln cross-posted

Cross-posting: large % of articles are cross-posted to multiple groups. Somebody reading one group may “reply-to-all” such that all groups see it.

Structural analysis: how do edges btw authors form? How does the reciprocity of groups compare? How can we measure similarity btw groups?

-Make network of authors for each group, if a_1 has replied to a_2 at any point, there is an edge from a_1 to a_2. Find power law relationship btw #of nodes and #edges over time (similar to Leskovec et al. densification). Exception: tw.bbs

-Reciprocity: which groups have highest reciprocity? Top 10 were European newsgroups, e.g. hun.politika (up to .58). Lowest reciprocity: tw.bbs

-Similarity: use Jaccard coefficient for cross-posts = #shared articles btw 2 groups / Total # articles in groups, can do same with shared authors

Highest similarity ~.54 (bc.politics and on.politics).

Draw thresholded similarity network, find clusters: parties, US regional, countries, alt.politics subgroups

-Image: english-speaking countries cluster. Can.politics (Canada) highly central!

Ownership Model: we would like to find out in which group the activity is truly occurring. How can we trace this? ANswer: assign “ownership” based on authors of posts. First, assign authors to groups based on devotion, where devotion(a,g): what % of an author a’s posts are exclusively posted to a given group g

-For all groups that author posts particular post p to, the post belongs to the group with the highest (normalized) ownership between it and the author

-Example: “Kiss the National Parks Good-Bye” initially corss-posted to several groups, 38 groups in total, ownership concentrated in seattle.politics and or.politics

Information flow between groups: How often does an author in group 1 respond to a post in group 2? Define influence g_a, g_b as the product of the groups’ devotion scores for a particular author

Ownership-based similarity. Q: How can ownership help us more precisely state when 2 groups are similar? Use devotion instead of Jaccard to calc similarity between groups

-Potential applications: link prediction, IR and relevance, ownership for email lists. Future work: use ownership to predict whether group will continue or die off

1.30pm Does Showing off Help to Make Friends? (Christophe Aguiton)

Self exposition and social capital:

-What do we let others see about ourselves on social networking sites?

-How do we relate to others depending on what they show?

Game sociological survey: link

part 1: if you were on these pics, which would you publish on a website?

part 2: questionnaire

part 3: down-to-one-friend (start with x friends, see pics only, eliminate one; add favorites info, elminate one; etc. down to one friend)

first launch: FB, diffuses to blogs, Flickr, news, 15,000 respondents by end of experiment.

-Sample is not representative of French SNS users. Lots of heavy internet users. 71.1% male, average age 28 years old, 47% high school diploma, 33% students.

First Results:

-moderate / controlled level of exposure (exposure score: ~2.4 on 1-4 scale)

-extraversion index, socializing index

Method: PCA to cluster photos in the experimental dataset. Four components: traditional self-exhibition (ordinary life situation), bodily immodesty (nudity / sexual situations), showing off (protests, etc.), provocative (negative activity).

-Cluster analysis with scores of PCA, five clusters: Modest (people don’t like to show themselves, 19%, more women, older, high level of ed, high status position, few friends) + four from above.

-Main question: find no correlation between sns use and level of self-exposition

-2nd question: how do people make friends?

- popular friendship targets (from 3rd part of game) are young, cool, active, unpopular are  older, more reserved

- subject choice largely guided by homophily, a tendency to bond with similar others. Results: people preferentially choose as friends of same age and diploma level. Heterophily by gender: both men and women choose women over men.

- What aspects of persona do different kinds of people look at? Modest people most closely look at “about me,” traditional exhibitionists most closely look at “wall,” provocative most closely look at “sexual preferences”

Main results of survey:

-Self-exposition on web is a social construction, requires reflexive and strategic control of one’s image, self-exhibition strategies differ according to sociological factors, social networks encourage homophily but also allow users to have more heterogeneous social capital

2pm. What are they blogging about? Personality, topic and motivation in blogs (Alastair J. Gill et al.)

How does personality influence blogger motivation?

Personality – describes fundamental core of individuals

-Behavior and preferences

-Useful for categorising users and consumers

-How does this influence bloggers? Blogs – unique freedom of expression for authors

-Already shown to influence langauge in CMC (Gill 2004, Nowson 2006).

-Analysis of Polish blogs w/ suggested psychological profiles)

Motivations: Internal – Documenting life, catharsis (therapy); External using own perspective – Interests, Opinions

Personality: Big Five model of personality (Goldberg ’92, Costa and McCrae ’92).

Data and Method: Internet meme personality test: 5 Y/N questions each for the Big Five personality types -> high-mid-low scores; 3 months of blogs extracted from Nielsen BuzzMetrics data. Basic statistics, text analysis.

results:

Neuroticism: use of blogs for self-therapy/catharsis – focusing on self and venting purely negative feelings

Extraversion: life narrative (documentation) in conversation with reader; expressing highs and lows, but not mundane. Use of 2nd person pronouns

Openness: review or evaluation of leisure (music, TV) from personal perspective, but no increase in thinking or senses

Conscientiousness: faithfully document life going on; references to others; positive emotion. Job focus, little temporal narrative.

Agreeableness: positive self-talk focus

Discussion: Blogs unsurprising mainly focus on self. Face apparently genuine in blogs. Agreeable bloggers provide a barometer of what is / isn’t acceptable in blogs

2.30pm A social identity approach to identify familiar strangers in a social network (Nitin Agrawal)

Who are familiar strangers?

Observe repeatedly, but do not know each other: Real world – people you see daily on a train (going to same workplace); Blogosphere – people who have similar blogging behavior / interests but not in each other’s social networks

Together, familiar strangers form a critical mass: understanding of one blogger gives a sensible and representative glimpse to others -> better customization, personalization and recommendation.

Familiar strangers in social media: an example, u is a blogger with interests A_u, friends v_1… v_k with interests A_v_1… A_v_k. Find non-adjacent u’ with similar interests (intersection of A_u, A_u’ is non-empty).

-Egocentric network view (exposure to network limited to neighbors).

-Social identity approach: cluster contacts into groups, propagate search through relevant clusters of contacts (prunes search space). For this to work, network needs to be a small world (WS 98)

-Method: represent contact by tag vector, content vector, use cosine similarity, then k-means clustering

-Ground truth: Global network view. Data: Blogcatalog (~24k nodes), DBLP (~35k nodes). Also compare to exhaustive and random search strategies.

Results: 79.3%+-3 for BlogCatalog, 91.3%+-2.1 for DBLP, greatly reduced search space.

3pm You are where you edit: Locating Wikipedia Contributors through Edit histories (Michael Lieberman, Jimmy Lin)

Minig Wikipedia: id Wikipedia contributors who edit geopages in a constrained space, have specific “pet” geopages (pages for geographical locations identified with geotags)

Features with extent: all geopages tagged with single lat/lon, even though they can be countries, cities, rivers, etc.

Wikiepdia edit histories: ignored anon edits, minor edits, focused on edits to geopages

Edit area = convex hull of geotags smaller than 1 degree sq. Account for outliers with simple approximator that cuts off at F closest-together geotags

Results: Pet Geopages. Over 50% of contributors with 5-20 edits, and 25% of contributors with over 20 edits, have 80% of edits to 1 or 2 geopages

Reasons for Tight Edit areas: randomly selected 100 contributors with at least 10 edits to geopages and small edit areas. Concurrently examined contributors’ user pages and the set of edited geopages to determine an interest. Contributors with small edit areas tend to be born in or are living in close-to-edit areas.

Future work: using alterante measures to determine geopage edit significance

4pm CourseRank: a closed-community social system through the magnifying glass(Georgia Koutrika)

CourseRank: community for Stanford students to evaluate courses, browse courses, plan academic program, interact with each other, ask / answer questions. 1.5 years, 11k students, 19k courses, 3k reviews

Special features: well-defined closed community, multiple constituencies (staff, students), special-purpose tools, hybrid data

A new class of social sites defined by these characteristics. E.g. university social site, scientific social site, A-space (intelligence)

Popularity: >85% of Stanford students are CourseRank users

Usage: follows academic cycle

Participation inequality: 20% created by intermittent, 80% by active; 31% of lurkers, 38% intermittent, 30% (!) active

Smaller communities (departments) breed more active students

Truths and Lies: grade distribution follows official. Good incentives make better users (is this really evidence?). But there is bias: correlation between grade given to student and rating given by student

Lessons Learned:

-added-value services a big thing

-high-quality data

-community feeling is strong = students coming together with common need

-meaningful incentives

4.30pm Using transactional information to predict link strength in online social networks (Indika Kahanda)

OSNs (Online Social Networks) are larger and more heterogeneous than manually-collected social networks

High median degree implies presence of many weak links

Conjecture: Link strength can be predicted from transactional information

Data: Purdue FB. Transactional info: Wall comm, photo postings, group memberships. Networks over Wall, Pictures look more like offline-collected networks (e.g. AdHealth data)

Automatically identifying top friends: link strength prediction task (binary)

Related to, but different from, link prediction (which focuses on predicting future links between u,v in a unimodal network). Previous approaches use attribute similarity features or topological features of network. Adamic and Adar (’03) used ancillary networks but focused on similarity vs. transaction

Feature types: Attribute-based (attribute similarity btw two nodes), Topological features (assess connectivity of users in friendship network), transactional features (number of bi-directional wall/photo/group posts), network-transactional features (assess connectivity of users in transaction networks)

Experiment 1: Feature rankings. Compare relative importance of each of 50 features, using info gain and chi-square statistic. 12 of top 15 are network-transactional features, 3 are transactional, 12 use wall info, 3 use picture info.

Experiment 2: Feature type comparison. Ablation study. Network-transactional features achieve best performance

Experiment 3: Link type comparison. Ablation study using data from each link type separately (all features). Wall information results in best performance. Picture info does not improve performance because of sparsity

Experiment 4: overall classification results. Bagged decision trees perform best.

Results indicate that transactional events useful for presenting link strength, but should be used in context of larger network for best performance

5pm RevRank: a fully unsupervised algorithm for selecting the most helpful book reviews (Oren Tsur)

Most reviews are: repetitive, limited contribution, poorly written, unnoticed

User voting bias: Liu et al. – imbalance vote bias, early bird bias, winner circle bias. Many very helpful reviews go unnoticed.

Interesting features of reviews:

-there are a lot of them

-contributors put big cognitive effort to generate them

-Good faith. Reviewers expect no direct reward.

Main idea: automatic detection of dominant concepts. Dominant concepts are either really frequent or infrequent but very informative. Term dominance defined as ratio of term frequency in review set to term frequency in balanced review set (British National Corpus)

RevRank algorithm: find most dominant concept, vectorize, rank reviews according from centroid identified by the core vector

Experimental setup: 12k reviews for Da Vinci Doe, World is Flat, Harry Potter, Ender’s Game. Compared to random, user votes. Gold standard – human labels.

Results: in 85% of test batches, RevRank pick was ranked “the most helpful.” In some cases, random algorithm outperformed user votes!

Summary: RevRank is fully unsupervised, better than user votes, finds “hidden” reviews and interesting insights

End of Day 1

Our (Vladimir D. Barash, Marc Smith, Lise Getoor, Howard T. Welser ) poster paper, Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context  has been accepted and published at the 2009 ICWSM (International Conference on Weblogs and Social Media) conference which will be held in San Jose, California this May 17, 2009 – May 20, 2009.

Abstract
Social media communities (e.g. Wikipedia, Flickr, Live Q&A) give rise to distinct types of content, foremost among which are relational content (discussion, chat) and factual content (answering questions, problem-solving). Both users and researchers are increasingly interested in developing strategies that can rapidly distinguish these types of content. While many text-based and structural strategies are possible, we extend two bodies of research that show how social context, and the social roles of answerers can predict content type.  We test our framework on a dataset of manually labeled contributions to Microsoft’s Live Q&A and find that it reliably extracts factual and relational messages from the data.

Full Text: PDF: 2009 ICWSM Distingusihing knowledge versus social capital

With a  very nice letter that puts the award in some context:

Ten papers out of 515 at the conference were selected for Best Paper Awards.  Many thanks to track organizers Karrie Karahalios and Fernanda Viegas.

A previous paper in 2006 also got best paper: You Are Who You Talk To: Detecting Roles in Usenet Newsgroups, by Danyel Fisher, Marc Smith, and Howard T. Welser

Two years before that in 2004 Fernanda Viegas and I also published a paper at HICSS that got best paper: Newsgroup Crowds and AuthorLines: Visualizing the Activity of Individuals in Conversational Cyberspaces,
by Ferndanda B. Viégas and Marc Smith.

Tom Erickson maintains a great listing of many years of HICSS papers.

Here is Tom Lento receiving the award at the conference in Hawaii earlier this month:

More Best Papers from this year…

	Collaboration Systems and Technologies Track Minitrack: Cross-Organizational and Cross-Border IS/IT Collaboration Paper Title: Breaching the Knowledge Transfer Blockade in IT Offshore Outsourcing Projects – A Case from the Financial Services Industry Authors: Robert Gregory, Roman Beck, and Michael Prifling
	Decision Technologies and Service Sciences Minitrack: Information Systems for Sustainable Development Paper Title: Sustainability of Grasshopper Management and Support through CARMA Authors: John Hastings, Alexandre Latchininsky, and Scott Schell
	Digital Media: Content and Communication Minitrack: Social Spaces: Production and Consumption of Goods in Digital Collectives Paper Title: A Conceptual and Operational Definition of ‘Social Role’ in Online Community Authors: Eric Gleave, Howard Welser, Thomas Lento, and Marc Smith
	Electric Power Systems Restructuring Reliability and Cyber Security Paper Title: Infrastructure Protection in the Ancient World Author: Michael Assante
	Electronic Government Minitrack: E-Government Organization and Management Co-chairs: Helmut Krcmar, J. Ramon Gil-Garcia, and Maddalena Sorrentino Paper Title: Understanding the “Boundary” in Information Sharing and Integration Authors: Lei Zheng, Tung-Mou Yang, Theresa Pardo, and Yuanfu Jiang
	Information Technology in Healthcare Minitrack:T Adoption and Evaluation in Healthcare Paper Title: Methodological Approaches to Measuring the Effects of Implementation of Health Information Technology (HIT) Authors: Lance Roberts, Marcia Ward, Jane Brokel, Douglas Wakefield, Donald Crandall, and Paul Conlon
	Internet and the Digital Economy Minitrack: Social Networks and Virtual Worlds for Work, Learning and Play Paper Title: Cyber Migration: An Empirical Investigation on Factors that Affect Users’ Switch Intentions in Social Networking Sites Authors:Zengyan Cheng, Yinping Yang, and John Lim
	Knowledge Management Systems Minitrack: Knowledge Management Tools, Technologies, Mashups, and Design Approaches Co-chairs: Stefan Smolnik, Nassim Belbaly, and Richard Orwig Paper Title: The Visual Wiki: A New Metaphor for Knowledge Access and Management Authors: Christian Hirsch, John Hosking, John Grundy, Tim Chaffe, David MacDonald, and Yuriy Halytskyy
	Organizational Systems and Technology Social Issues in Information Technology Paper Title: Making it Hard to Lie: Cultural Determinants of Media Choice for Deception Authors: Christopher Furner and Joey George
	Software Technology Minitrack: Architectures for Distributed Systems Paper Title: Celebrating Diversity in Volunteer Computing Authors: David Anderson and Kevin Reed

Knowledge sharing and Yahoo Answers: Everyone knows something

In particular, this image (figure 4) is a great use of an innovative way of handling large network graphs: chop them into a matrix of ego-net thumbnails.

Adamic et al. WWW 2008 Yahoo Answers Roles and Tag Ecologies

This is a neat way to side step the “blob” problem of many directed graph visualizations: too many nodes and too many links make the image impossible to understand.

Each grid of images represents a collection of authors who share contribution to questions with the same tag.  In this case the programming, wrestling, and marriage tags.  Each grid is a collection of ego-centric network diagrams, each author is displayed with their “1.5 degree” connections: their links to friends and thei friend’s links to one another.

Display a collection of authors and contrast multiple collections and  several interesting observations are possible.  First, not everyone who contributes to a tag is the same, a few highly active people make significant contributions while most people are lightly connected and make modest contributions.  Second, not everyone who contributes heavily does so in the same way.   In the upper left hand corner of each grid is the “top” person in that sample of the tag population.  Each is highly active but create different types of patterns of connection through that activity.  In programming the top person is a classic “answer person” – high out degree, low in-degree, connected to isolates and a resulting low clustering coefficient.  The top contributor in the Marriage tag is different, however: most of the people they connect to are connected to the other people they connect to: their “friends are friends”.  Their clustering coefficient is comparatively high in contrast with the top contributor to the “Programming” tag.  The top contributor of questions with the “Wrestling” tag is a hybrid: the author maintains a cluster of highly inter-connected repeat discussion partners while replying to a population of question people like a classic “answer person”.

It is worth noting the marriage resembles wrestling more than programing.

It is also worth noting that this visualization approach, while not perfect, is a nice step forward for information visualizations of complex graphs.  Graph vizualization has been stuck for many years: complex graphs are hard to draw in meaningful ways, let alone to do so automatically. This approach side-steps many of the obstacles to the main approach of whole graph visualization to focus on attributes of individuals and distributions of network variation.

The NodeXL add-in for Excel can generate these sub-graphs for any network: select “Insert subgraph images”.  The thumbnail of each node’s “egonet” is inserted in the spreadsheet and can be written to a local directory and later stitched into an array.

NodeXL Subgraph images