“Digg is a community-based popularity website with an emphasis on technology and science articles, recently expanding to a broader range of categories such as politics and entertainment. It combines social bookmarking, blogging, and syndication with a form of non-hierarchical, democratic editorial control.
News stories and websites are submitted by users, and then promoted to the front page through a user-based ranking system. This differs from the hierarchical editorial system that many other news sites employ.” (The above is from the Wikipedia Definition of Digg and has been added to give a point of reference to those not familiar with Digg and its applications)
Digg functions democratically, allowing any and all users to vote on sites and attempt to “digg up” the article. Typically an article will be brought to the top based upon the quality of the content however the algorithm involves the frequency to which it is linked to from other “dugg” articles and views in general.
Several interesting social phenomena occur in the Digg.com setting which place interesting twists on the analysis of information cascades that we have done in class. Primarily the existence of “top diggers” drastically effects the principals learned thus far about information cascades. “Top diggers” are individuals who, through an alternate algorithm involving the popularity of articles they have “dugg” and their experience, have added weight to their votes. In this setting if we view the sequence of votes as a network of information, votes [guesses] by several “top diggers” can cause a cascade that would normally not occur. Because of conformity and the resulting information cascade it is very easy for “top diggers” to cause cascades for sub-par articles with their persistency or the weight of their votes. The probabilities the other voters would normally view still place emphasis on voting rather than not voting even though voting is not the preferred or “rational” action, as described by the author.
These probabilities, which we learned in class can be used to determine subsequent guesses of individuals in the network, are incredibly skewed due to the value of certain votes [guesses] over others. Unlike the situation viewed in class, the probability that an article is worth a vote will always be the greatest, conditioned upon the votes [guesses] of the “top diggers.” As a result, as stated before, it will always be in the best interest in the other voters to vote for the article, therefore causing the resulting cascade.
This article is a very interesting analysis of information cascades with additional information. As well, this article touches upon other types of information cascades, specifically “The Urn Game,” which is the basis for the digg.com analysis.
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