Verifying social network models of Wikipedia knowledge community

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• 作者：Michał Jankowski-Lorek^a ; ^{m.jankowski@pjwstk.edu.pl" class="auth_mail" title="E-mail the corresponding author} ; Szymon Jaroszewicz^b ; ^c ; ^{s.jaroszewicz@ipipan.waw.pl" class="auth_mail" title="E-mail the corresponding author} ; Łukasz Ostrowski^d ; ^{lukasz.ostrowski@uw.edu.pl" class="auth_mail" title="E-mail the corresponding author} ; Adam Wierzbicki ; ^e ; ^{adamw@pjwstk.edu.pl" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Wikipedia ; Social network ; Verification ; Behavioral network ; Survey ; Social dimension
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：20 April 2016
• 年：2016
• 卷：339
• 期：Complete
• 页码：158-174
• 全文大小：2249 K

文摘

The Wikipedia project has created one of the largest and best-known open knowledge communities. This community is a model for several similar efforts, both public and commercial, and even for the knowledge economy of the future e-society. For these reasons, issues of quality, social processes, and motivation within the Wikipedia knowledge community have attracted attention of researchers. Research has often used Social Network Analysis applied to networks created based on behavioral data available from the edit history of the Wikipedia.

This paper asks the following question: are the popular assumptions about the social interpretations of networks created from the edit history valid? We verify commonly assumed interpretations of four types of networks created from discussions on Wikipedia talk pages, co-edits and reverts in Wikipedia articles, and edits of articles in various topics, by comparing these networks with results from a survey of editors of the Polish Wikipedia community. The results indicate that while the behavioral networks are strongly related to the declarations of respondents, only in one case of the network created from talk pages and interpreted as acquaintance we can observe a near equivalence. The article next describes improved definitions of behavioral indicators obtained through machine learning. The improved networks are much closer to their declarative counterparts.

The main contribution of the article is a validated model of an acquaintance network among Wikipedia editors that can be derived from behavioral data and validly interpreted as acquaintance. Other contributions are improved versions of behavioral networks based on editing behavior and discussion history on the Wikipedia.