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Modeling user reputation in wikis
Abstract Collaborative systems available on the WebCollaborative systems available on the Web allow millions of users to share information through a growing collection of tools and platforms such as wikis, blogs, and shared forums. By their very nature, these systems contain resources and information with different quality levels. The open nature of these systems, however, makes it difficult for users to determine the quality of the available information and the reputation of its providers. Here, we first parse and mine the entire English Wikipedia history pages in order to extract detailed user edit patterns and statistics. We then use these patterns and statistics to derive three computational models of a user's reputation. Finally, we validate these models using ground-truth Wikipedia data associated with vandals and administrators. When used as a classifier, the best model produces an area under the receiver operating characteristic (ROC) curve (AUC) of 0.98. Furthermore, we assess the reputation predictions generated by the models on other users, and show that all three models can be used efficiently for predicting user behavior in Wikipedia.for predicting user behavior in Wikipedia.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion In this paper, we have modeled user reputaIn this paper, we have modeled user reputation in wiki systems. We have presented 3 reputation models. According to Model 1, when a user inserts a piece of content in a wiki page his reputation is increased and when a piece of content contributed by the user is deleted by another user, his reputation is decreased. In Model 2, we also take into account the time interval between insertions and deletions of content items. Finally, in Model 3, we add another parameter which is the current reputation of the deleter. Our experiments show that the three models can accurately assign reputation values to Wikipedia's known administrators/good users and vandals/blocked users. Additional analyses reveal that Model 1 does slightly better at detecting vandals and Model 3 does slightly better at detecting good users. The proposed models can be applied in real time to calculate dynamic and individualized reputation values. While Model 1 is simpler to implement, Model 3 appears to be slightly more accurate, and more robust to attacks than several other competing models of reputation.eral other competing models of reputation.
Data source Survey responses  +
Doi 10.1002/sam.10070 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Modeling%2Buser%2Breputation%2Bin%2Bwikis%22  +
Has author Sara Javanmardi + , Cristina Lopes + , Pierre Baldi +
Has domain Computer science +
Has topic Vandalism + , Reputation systems +
Issue 2  +
Pages 126-139  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in Statistical Analysis and Data Mining +
Research design Statistical analysis  +
Research questions Here, we first parse and mine the entire EHere, we first parse and mine the entire English Wikipedia history pages in order to extract detailed user edit patterns and statistics. We then use these patterns and statistics to derive three computational models of a user’s reputation. Finally, we validate these models using ground–truth Wikipedia data associated with vandals and administrators.ssociated with vandals and administrators.
Revid 10,876  +
Theories Undetermined
Theory type Design and action  +
Title Modeling user reputation in wikis
Unit of analysis Edit  + , User  +
Url http://dx.doi.org/10.1002/sam.10070  +
Volume 3  +
Wikipedia coverage Sample data  +
Wikipedia data extraction Live Wikipedia  +
Wikipedia language English  +
Wikipedia page type Other  +
Year 2010  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:29:43  +
Categories Vandalism  + , Reputation systems  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:29:51  +
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