A Model for Information Growth in Collective Wisdom Processes

ACM Transactions on Knowledge Discovery from Data (TKDD)
Volume 6,  Issue 2   July 2012
Sanmay Das, Malik Magdon-Ismail
http://dl.acm.org/tipsvc.cfm?id=2297458&code=%27%2A%3C3%2AQ%3CC%272%40%20%20%0A&tick=D6F962ED2A53E623CBF86BD91D9E5255

“Collaborative media such as wikis have become enormously successful venues for information creation. Articles accrue information through the asynchronous editing of users who arrive both seeking information and possibly able to contribute information. Most articles stabilize to high-quality, trusted sources of information representing the collective wisdom of all the users who edited the article. We propose a model for information growth which relies on two main observations: (i) as an article’s quality improves, it attracts visitors at a faster rate (a rich-get-richer phenomenon); and, simultaneously, (ii) the chances that a new visitor will improve the article drops (there is only so much that can be said about a particular topic). Our model is able to reproduce many features of the edit dynamics observed on Wikipedia; in particular, it captures the observed rise in the edit rate, followed by 1/t decay. Despite differences in the media, we also document similar features in the comment rates for a segment of the LiveJournal blogosphere”.

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