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  1. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (WebKDD/SNA-KDD '07)
  2. Dynamics of collaborative document rating systems
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Learning to rank typed graph walks: local and global approaches
Exploiting time-varying relationships in statistical relational models
Community detection in large-scale social networks
Node roles and community structure in networks
Inferring agent dynamics from social communication network
Dynamics of collaborative document rating systems
Why we twitter: understanding microblogging usage and communities
Looking for great ideas: analyzing the innovation jam
DBconnect: mining research community on DBLP data
Spectral clustering in telephone call graphs
Applying link-based classification to label blogs
A recommender system based on local random walks and spectral methods
Automated social hierarchy detection through email network analysis

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Dynamics of collaborative document rating systems

Content Provider ACM Digital Library
Author Lerman, Kristina
Abstract The rise of social media sites, such as blogs, wikis, Digg and Flickr among others, underscores a transformation of the Web to a participatory medium in which users are actively creating, evaluating and distributing information. The social news aggregator Digg allows users to submit links to and vote on news stories. Like other social media sites, Digg also allows users to designate others as "friends" and easily track friends' activities: what new stories they submitted, commented on or liked. Each day Digg selects a handful of stories to feature on its front page. Rather than rely on the opinion of a few editors, Digg aggregates opinions of thousands of its users to decide which stories to promote to the front page. We construct a mathematical model to study how collaborative rating and promotion of news stories emerges from independent decisions made by many users. The model takes into account user behavior: e.g., whether they read stories on the front page or through the Friends interface. Solutions of the model qualitatively reproduce the observed dynamics of votes received by actual stories on Digg. Digg also ranks users according to how successful they are in getting their stories promoted to the front page. We create a model that describes how a user's rank changes in time as he gets more stories to the front page and becomes more influential in the community. We find qualitative agreement between predictions of the model and the evolution of rank for Digg users. The Digg model of allowing users to collectively evaluate how interesting the news stories are can be generalized to collaborative evaluation of the quality of information. Mathematical analysis can be used as a tool to explore different voting methods to select the most effective one before the method is ever implemented in a real system.
Starting Page 46
Ending Page 55
Page Count 10
File Format PDF
ISBN 9781595938480
DOI 10.1145/1348549.1348555
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2007-08-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword News aggregation Mathematical analysis Social networks Collaborative rating
Content Type Text
Resource Type Article
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