On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
He says, she says: conflict and coordination in Wikipedia
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The collaborative organization of knowledge
Communications of the ACM - Designing games with a purpose
Popularity, novelty and attention
Proceedings of the 9th ACM conference on Electronic commerce
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Model for Information Growth in Collective Wisdom Processes
ACM Transactions on Knowledge Discovery from Data (TKDD)
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Wikis and blogs have become enormously successful media for collaborative information creation. Articles and posts 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 and on blogs collected from LiveJournal; in particular, it captures the observed rise in the edit rate, followed by 1/t decay.