Why social networks grow or fail ? A graphic approach to understand the rules

Posted on November 3, 2006. Filed under: English |

Reading the very good Information Design Watch blog this morning I learned about 2016, a symposium in Washington that took place in October. Organized by The Computer Science and Communication Board (CSTB) for its 20th anniversary (see agenda), hosting lecturers such as the head of Yahoo Research, the CEO of Google or major academic figures. As an article by the NY Times reported it, one of the key subject was the analysis of how computer science and other scientific areas permeated each other more and more over the last decades. One of its topics was social communities, and while this has been a subject long analyzed by anthropologists and sociologists, the success of online social networks of the likes of Friendster, Linkedin or MySpace has added another perspective to it. An anthropologist, Karen Stephenson, made the link in the corporate culture (see Business 2.0 article), as more and more want to use the power of those tools to leverage Communities of Practice (CoP, see more on that subject on the APQC website).

The emergence of such networks in real life usually took many years, whereas online it can take less than a year for a site to create a massive community of readers, that even when it lacks typical social networks features like a blog will do (see example of PopSugar celebrity gossip site attracting more than 12 million pageviews in less than 5 months, as featured in a Business 2.0 article). Understanding the drivers to creating, growing and maintaining such an online community is a hot subject amongst publishers and advertisers. One often favored approach is the massive log data analysis … which in turn ask the question of extracting useful information out of it. Common marketing/CRM datamining tools where not build for such massive multilayered data sets, and picking which angle to use to analyze it may prove quite hard … or lucky ! This is why a dynamic graphical approach could do the trick better, as you can see in the example shown by the NY Times. The objective beeing to derive an algorithmic approach that can be replicated.

The web as a living organism, by Jon Kleinberg and Lars Backstrom, Cornell University



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