Difference between revisions of "Structure-Based Features for Predicting the Quality of Articles in Wikipedia"
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| − | '''Structure-Based Features for Predicting the Quality of Articles in Wikipedia''' - scientific work related to Wikipedia quality published in 2017, written by Baptiste de La Robertie, Yoann Pitarch and Olivier Teste. | + | '''Structure-Based Features for Predicting the Quality of Articles in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Baptiste de La Robertie]], [[Yoann Pitarch]] and [[Olivier Teste]]. |
== Overview == | == Overview == | ||
| − | Success of Wikipedia is decidedly due to the free availability of high quality articles across many different expertise areas. If most of these resolute collaborations between authoritative users might constitute referenceable sources, Wikipedia is not sheltered from well-identified problems regarding articles quality, e.g., reputability of third-party sources and vandalism. Because of the huge number of articles and the intensive edit rate, it is not reasonable to even consider the manual evaluation of the content quality of each article. In this paper, authors tackle the problem of modeling and predicting the quality of articles in collaborative platforms. Authors propose a quality model integrating both temporal and structural features captured from the implicit peer review process enabled by Wikipedia. A generic HITS-like framework is developed and able to capture both the quality of the content and the authority of the associated authors. Notably, a mutual reinforcement principle held between articles quality and author’s authority is exploited in order to take advantage of the collaborative graph generated by the users. Experiments conducted on a set of representative data from Wikipedia show the effectiveness of the computed indicators both in an unsupervised and supervised scenario. | + | Success of [[Wikipedia]] is decidedly due to the free availability of high quality articles across many different expertise areas. If most of these resolute collaborations between authoritative users might constitute referenceable sources, Wikipedia is not sheltered from well-identified problems regarding articles quality, e.g., reputability of third-party sources and vandalism. Because of the huge number of articles and the intensive edit rate, it is not reasonable to even consider the manual evaluation of the content quality of each article. In this paper, authors tackle the problem of modeling and predicting the quality of articles in collaborative platforms. Authors propose a quality model integrating both temporal and structural [[features]] captured from the implicit peer review process enabled by Wikipedia. A generic HITS-like framework is developed and able to capture both the quality of the content and the authority of the associated authors. Notably, a mutual reinforcement principle held between articles quality and author’s authority is exploited in order to take advantage of the collaborative graph generated by the users. Experiments conducted on a set of representative data from Wikipedia show the effectiveness of the computed [[indicators]] both in an unsupervised and supervised scenario. |
Revision as of 09:29, 14 August 2020
Structure-Based Features for Predicting the Quality of Articles in Wikipedia - scientific work related to Wikipedia quality published in 2017, written by Baptiste de La Robertie, Yoann Pitarch and Olivier Teste.
Overview
Success of Wikipedia is decidedly due to the free availability of high quality articles across many different expertise areas. If most of these resolute collaborations between authoritative users might constitute referenceable sources, Wikipedia is not sheltered from well-identified problems regarding articles quality, e.g., reputability of third-party sources and vandalism. Because of the huge number of articles and the intensive edit rate, it is not reasonable to even consider the manual evaluation of the content quality of each article. In this paper, authors tackle the problem of modeling and predicting the quality of articles in collaborative platforms. Authors propose a quality model integrating both temporal and structural features captured from the implicit peer review process enabled by Wikipedia. A generic HITS-like framework is developed and able to capture both the quality of the content and the authority of the associated authors. Notably, a mutual reinforcement principle held between articles quality and author’s authority is exploited in order to take advantage of the collaborative graph generated by the users. Experiments conducted on a set of representative data from Wikipedia show the effectiveness of the computed indicators both in an unsupervised and supervised scenario.
