CMS Wire reviews Telligent Social Media reporting and platforms

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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 PetouhoffForrester 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

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? ( 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: in particular 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 ( 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:

Deanonymizing Social Networks – Arvind Narayanan & Vitaly Shmatikov

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

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.