Predicting positive and negative links in online social networks

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Predicting positive and negative links in online social networks
Authors: Jure Leskovec, Daniel Huttenlocher, Jon Kleinberg [edit item]
Citation: WWW '10 Proceedings of the 19th international conference on World wide web  : 641-650. 2010.
Publication type: Conference paper
Peer-reviewed: Yes
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Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Yes
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Predicting positive and negative links in online social networks is a publication by Jure Leskovec, Daniel Huttenlocher, Jon Kleinberg.


[edit] Abstract

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). 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.

[edit] 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)."

Research details

Topics: Other collaboration topics [edit item]
Domains: Information systems [edit item]
Theory type: Explanation [edit item]
Wikipedia coverage: Case [edit item]
Theories: "Undetermined" [edit item]
Research design: Statistical analysis [edit item]
Data source: Websites, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Edit, User [edit item]
Wikipedia data extraction: Dump [edit item]
Wikipedia page type: Other [edit item]
Wikipedia language: Not specified [edit item]

[edit] Conclusion

"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."

[edit] Comments

"Data 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)"


Further notes[edit]

Facts about "Predicting positive and negative links in online social networks"RDF feed
AbstractWe 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 teamYes +
Collected data time dimensionCross-sectional +
CommentsData 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)
ConclusionWe 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 sourceWebsites + and Wikipedia pages +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Predicting%2Bpositive%2Band%2Bnegative%2Blinks%2Bin%2Bonline%2Bsocial%2Bnetworks%22 +
Has authorJure Leskovec +, Daniel Huttenlocher + and Jon Kleinberg +
Has domainInformation systems +
Has topicOther collaboration topics +
Pages641-650 +
Peer reviewedYes +
Publication typeConference paper +
Published inWWW '10 Proceedings of the 19th international conference on World wide web +
Research designStatistical analysis +
Research questionsWe 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).
Revid10,910 +
TheoriesUndetermined
Theory typeExplanation +
TitlePredicting positive and negative links in online social networks
Unit of analysisEdit + and User +
Urlhttp://dl.acm.org/citation.cfm?id=1772756 +
Wikipedia coverageCase +
Wikipedia data extractionDump +
Wikipedia languageNot specified +
Wikipedia page typeOther +
Year2010 +