Difference between revisions of "Leveraging Editor Collaboration Patterns in Wikipedia"
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| + | | title = Leveraging Editor Collaboration Patterns in Wikipedia | ||
| + | | date = 2012 | ||
| + | | authors = [[Hoda Sepehri Rad]]<br />[[Aibek Makazhanov]]<br />[[Davood Rafiei]]<br />[[Denilson Barbosa]] | ||
| + | | doi = 10.1145/2309996.2310001 | ||
| + | | link = https://dl.acm.org/citation.cfm?doid=2309996.2310001 | ||
| + | }} | ||
'''Leveraging Editor Collaboration Patterns in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Hoda Sepehri Rad]], [[Aibek Makazhanov]], [[Davood Rafiei]] and [[Denilson Barbosa]]. | '''Leveraging Editor Collaboration Patterns in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Hoda Sepehri Rad]], [[Aibek Makazhanov]], [[Davood Rafiei]] and [[Denilson Barbosa]]. | ||
== Overview == | == Overview == | ||
Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. Authors investigate this problem in the context of the collaborative editing of articles in [[Wikipedia]], showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors. Authors train a model using a distant supervision approach, by labeling interactions between editors as positive or negative depending on how these editors vote for each other in Wikipedia admin elections. Authors use the model to predict the attitude among other editors, who have neither run nor voted in an election. Authors validate model by assessing its accuracy in the tasks of predicting the results of the actual elections, and identifying controversial articles. Authors analysis reveals that the interactions in co-editing articles can accurately predict votes, although there are differences between positive and negative votes. For instance, the accuracy when predicting negative votes substantially increases by considering longer traces of the edit history. As for predicting controversial articles, authors show that exploiting positive and negative interactions during the production of an article provides substantial improvements on previous attempts at detecting controversial articles in Wikipedia. | Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. Authors investigate this problem in the context of the collaborative editing of articles in [[Wikipedia]], showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors. Authors train a model using a distant supervision approach, by labeling interactions between editors as positive or negative depending on how these editors vote for each other in Wikipedia admin elections. Authors use the model to predict the attitude among other editors, who have neither run nor voted in an election. Authors validate model by assessing its accuracy in the tasks of predicting the results of the actual elections, and identifying controversial articles. Authors analysis reveals that the interactions in co-editing articles can accurately predict votes, although there are differences between positive and negative votes. For instance, the accuracy when predicting negative votes substantially increases by considering longer traces of the edit history. As for predicting controversial articles, authors show that exploiting positive and negative interactions during the production of an article provides substantial improvements on previous attempts at detecting controversial articles in Wikipedia. | ||
Revision as of 21:22, 12 October 2019
Authors | Hoda Sepehri Rad Aibek Makazhanov Davood Rafiei Denilson Barbosa |
|---|---|
Publication date | 2012 |
DOI | 10.1145/2309996.2310001 |
Links | Original |
Leveraging Editor Collaboration Patterns in Wikipedia - scientific work related to Wikipedia quality published in 2012, written by Hoda Sepehri Rad, Aibek Makazhanov, Davood Rafiei and Denilson Barbosa.
Overview
Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. Authors investigate this problem in the context of the collaborative editing of articles in Wikipedia, showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors. Authors train a model using a distant supervision approach, by labeling interactions between editors as positive or negative depending on how these editors vote for each other in Wikipedia admin elections. Authors use the model to predict the attitude among other editors, who have neither run nor voted in an election. Authors validate model by assessing its accuracy in the tasks of predicting the results of the actual elections, and identifying controversial articles. Authors analysis reveals that the interactions in co-editing articles can accurately predict votes, although there are differences between positive and negative votes. For instance, the accuracy when predicting negative votes substantially increases by considering longer traces of the edit history. As for predicting controversial articles, authors show that exploiting positive and negative interactions during the production of an article provides substantial improvements on previous attempts at detecting controversial articles in Wikipedia.
