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Predicting positive and negative links in online social networks
Abstract We study online social networks in which rWe study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.members of the surrounding social network.
Added by wikilit team Yes  +
Collected data time dimension Cross-sectional  +
Comments Data collection: "Using the latest completData collection: "Using the latest complete dump of Wikipedia page edit history (from January 2008) we extracted all administrator election and vote history data. This gave us 2,794 elections with 103,747 total votes and 7,118 users participating in the elections (either casting a vote or being voted on). Out of this total, 1,235 elections resulted in a successful promotion, while 1,559 elections did not result in the promotion of the candidate" (p. 643)n the promotion of the candidate" (p. 643)
Conclusion We find that the signs of links in the undWe find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.members of the surrounding social network.
Data source Websites  + , Wikipedia pages  +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Predicting%2Bpositive%2Band%2Bnegative%2Blinks%2Bin%2Bonline%2Bsocial%2Bnetworks%22  +
Has author Jure Leskovec + , Daniel Huttenlocher + , Jon Kleinberg +
Has domain Information systems +
Has topic Other collaboration topics +
Pages 641-650  +
Peer reviewed Yes  +
Publication type Conference paper  +
Published in WWW '10 Proceedings of the 19th international conference on World wide web +
Research design Statistical analysis  +
Research questions We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism).
Revid 10,910  +
Theories Undetermined
Theory type Explanation  +
Title Predicting positive and negative links in online social networks
Unit of analysis Edit  + , User  +
Url http://dl.acm.org/citation.cfm?id=1772756  +
Wikipedia coverage Case  +
Wikipedia data extraction Dump  +
Wikipedia language Not specified  +
Wikipedia page type Other  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:29:58  +
Categories Other collaboration topics  + , Information systems  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:30:27  +
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