Article

Encyclopedia of Social Network Analysis

By Marc Smith


My colleague George Barnett has edited the Encyclopedia of Social Network Analysis.

I contributed four entries with co-authors:

WWW Hyperlink Networks

with Robert Ackland, Australian National University

Email networks

with Derek Hansen, Brigham Young University

Blog networks

with John Kelly, Morningside Analytics, Harvard Berkman Center

Facebook networks

with Bernie Hogan, Oxford Internet Institute

Description:

This two-volume encyclopedia provides a thorough introduction to the wide-ranging, fast-developing field of social networking, a much-needed resource at a time when new social networks or “communities” seem to spring up on the internet every day. Social networks, or groupings of individuals tied by one or more specific types of interests or interdependencies ranging from likes and dislikes, or disease transmission to the “old boy” network or overlapping circles of friends, have been in existence for longer than services such as Facebook or YouTube; analysis of these networks emphasizes the relationships within the network. The Encyclopedia of Social Networks offers comprehensive coverage of the theory and research within the social sciences that has sprung from the analysis of such groupings, with accompanying definitions, measures, and research.

Featuring approximately 350 signed entries, along with approximately 40 media clips, organized alphabetically and offering cross-references and suggestions for further readings, this encyclopedia opens with a thematic reader’s guide in the front that groups related entries by topics. A chronology offers the reader historical perspective on the study of social networks. This two-volume reference work is a must-have resource for libraries serving researchers interested in the various fields related to social networks, including sociology, social psychology and communication and media studies.

Congressional Tweets Analyzed at UMD

By Marc Smith

Here is a great piece of social media research from the University of Maryland, College of Information Studies.

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.”

CMS Wire reviews Telligent Social Media reporting and platforms

By Marc Smith

Telligent Logo

There is some very positive coverage of Telligent recently in the press.  First there is the very detailed review of the community platform and its reporting features from Barb Mosher at CMS Wire: “Social Web Analytics with Telligent’s Harvest Reporting Server“.  The article does a great job of detailing the features of the Harvest social media analysis product.  The article’s explanation of what social media analytics are is itself useful:

“You need to measure what’s happening in your community. If you are interested in knowing what your community members are up to, what information they are sharing and looking at, what they are saying about you, your product or your service (positive and negative), then you need social analytics.

If you need to know how many users are signing up, how many are contributing to blogs, wikis, forums, how many are asking and answering questions, then you need social analytics.

With social computing becoming much more mainstream and in many cases, a requirement for both external and internal relationship building, it has become critical to measure the impact these solutions really have. You also need to know how and where to improve these solutions.

And you aren’t going to get this information from traditional web traffic analytics.”

A mention of Telligent in the New York Times is also cause for note: the recent article on help communities listed Telligent as a provider of platforms, along with Jive, Lithium, and HelpShare, that enable companies to host communities of passionate users who help one another solve problems with their products.  There is a great quote from Natalie Petouhoff, Forrester Research analyst:

Natalie L. Petouhoff, an analyst at Forrester Research, said that online user groups conform to what she calls the 1-9-90 rule. About 1 percent of those in the community, she explained, are super-users who supply most of the best answers and commentary. An additional 9 percent are “responders” who mainly reply and rate Web posts, she said, and the other 90 percent are “readers” who primarily peruse and search the Web site for useful information.

“The 90 percent will come,” Ms. Petouhoff said, “if you have the 1 percent.”

I would extend this point and add: within the 1% of active users are all the different types of active contribution, both good and bad.  Top answer people, discussion starters, discussion people, question people, and flame warriors all crowd into this sliver of the online demographic.  It is important to have the tools to separate the different kinds of active contributions to be sure that an active community is also a properly productive one!

Social Networks in the News at NYT

By Marc Smith

My colleague Scott Sargent at Telligent notes that there are two sections of the March 29th Sunday New York Times feature articles illustrated with network graphs.  The Business section runs an article “Is Facebook Growing Up Too Fast?“ (http://www.nytimes.com/2009/03/29/technology/internet/29face.html) and the Style Section has an article on The Celebrity Twitter Ecosystem.

20090329 NYT Facebook Ego Networks

20090329 NYT Facebook Ego Networks

My colleague Prof. Ben Shniederman is positively impressed by these images.  He writes, “Notice how the node layout remains stable as edges are removed, so by the 4th figure the edges can all be followed easily….”.  This is one of the themes he highlights in his paper and presentations about problems and improvements in network graph drawing (see: http://www.cs.umd.edu/hcil/nvss/and in particular http://www.cs.umd.edu/hcil/pubs/presentations/NVSS-3.ppt). Prof. Shniederman’s  5th edition of Designing the User Interface is now available with two full chapters on the website with wordles to open each chapter.

A somewhat related article ran the same day in the Style section on The Celebrity Twitter Ecosystem (http://www.nytimes.com/2009/03/29/fashion/29twitter.html). This image focused on the linkages between well known people using Twitter and, by extension, revealing who they follow and who follows them in the social network.

2009 -03- 29 - NYT - Twitter Ecosystem

In the first image no names are associated with the nodes, in the second the names are the major point of the diagram.

The practice of “anonymization” of network graphs may be moot in light of a recent publication mentioned on the Social Network Analysis email list (SOCNET) by Mark Round from QinetiQ of a paper:

De–anonymizing Social Networks – Arvind Narayanan & Vitaly Shmatikov
 (http://33bits.org/2009/03/19/de–anonymizing-social-networks/)

which suggests that just publishing the unique pattern of links around an individual is sufficient to identify them in an otherwise anonymized data base.

Abstract:
Operators of online social networks are increasingly sharing
potentially sensitive information about users and their relationships
with advertisers, application developers, and data-mining researchers.
Privacy is typically protected by anonymization, i.e., removing names,
addresses, etc.
We present a framework for analyzing privacy and anonymity in social
networks and develop a new re-identification algorithm targeting
anonymized social-network graphs. To demonstrate its effectiveness on
real-world networks, we show that a third of the users who can be
verified to have accounts on both Twitter, a popular microblogging
service, and Flickr, an online photo-sharing site, can be re-identified
in the anonymous Twitter graph with only a 12% error rate.
Our de-anonymization algorithm is based
purely on the network topology, does not require creation of a large
number of dummy “sybil” nodes, is robust to noise and all existing
defenses, and works even when the overlap between the target network
and the adversary’s auxiliary information is small.