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Co-authorship 2.0 : Patterns of collaboration in Wikipedia
The study of collaboration patterns in wikis can help shed light on the process of content creation by online communities. To turn a wiki's revision history into a collaboration network, we propose an algorithm that identifies as authors of a page the users who provided the most of its relevant content, measured in terms of quantity and of acceptance by the community. The scalability of this approach allows us to study the English Wikipedia community as a co-authorship network. We find evidence of the presence of a nucleus of very active contributors, who seem to spread over the whole wiki, and to interact preferentially with inexperienced users. The fundamental role played by this elite is witnessed by the growing centrality of sociometric stars in the network. Isolating the community active around a category, it is possible to study its specific dynamics and most influential authors.
Wikipedia and its network of authors from a social network perspective
2012 Fourth International Conference on Communications and Electronics (ICCE), 2012
Online social networks (OSNs) become more and more important in today's social and business life. Therefore, considerable effort is put in research to gain a deeper knowledge of the development of these networks and their dynamics. However, most of the existing literature is based on very limited subsets of the network data, which is often filtered by the OSN operator providing the data or biased by the crawling mechanisms used to obtain the data. This makes it difficult to analyze the temporal evolution of OSNs based on complete data. To overcome this issue, we investigate the dynamics of the publicly available collaboration network of the Wikipedia authors as an example for an OSN-like network. In particular, we study the temporal evolution of this network since its beginning and demonstrate that it exhibits prominent similarities to well known social networks such as the small-world phenomenon. This indicates that the insights gained from the analysis of Wikipedia's collaboration network might be transferable to social networks in general.
Network analysis of collaboration structure in Wikipedia
Proceedings of the 18th international conference on World wide web, 2009
In this paper we give models and algorithms to describe and analyze the collaboration among authors of Wikipedia from a network analytical perspective. The edit network encodes who interacts how with whom when editing an article; it significantly extends previous network models that code author communities in Wikipedia. Several characteristics summarizing some aspects of the organization process and allowing the analyst to identify certain types of authors can be obtained from the edit network. Moreover, we propose several indicators characterizing the global network structure and methods to visualize edit networks. It is shown that the structural network indicators are correlated with quality labels of the associated Wikipedia articles.
Multilevel Statistical Network Analysis of Wikipedia Coauthorship
2012
Prior scholarship on Wikipedia's collaboration processes has examined the properties of either editors or articles, but not the interactions between both. We analyze the coauthorship network of Wikipedia articles about breaking news demanding intense coordination and compare the properties of these articles and the editors who contribute to them to articles about contemporary and historical events. Using p*/ERGM methods to test a multi-level, multitheoretical model, we identify how editors' attributes and editing patterns interact with articles' attributes and authorship history. Editors' attributes like prior experience have a stronger influence on collaboration patterns, but article attributes also play significant roles. Finally, we discuss the implications our findings and methods have for understanding the socio-material duality of collective intelligence systems beyond Wikipedia.
2010
Wikipedia, the free online encyclopedia anyone can edit, is a live social experiment: millions of individuals volunteer their knowledge and time to collective create it. It is hence interesting trying to understand how they do it. While most of the attention concentrated on article pages, a less known share of activities happen on user talk pages, Wikipedia pages where a message can be left for the specific user. This public conversations can be studied from a Social Network Analysis perspective in order to highlight the structure of the "talk" network. In this paper we focus on this preliminary extraction step by proposing different algorithms. We then empirically validate the differences in the networks they generate on the Venetian Wikipedia with the real network of conversations extracted manually by coding every message left on all user talk pages. The comparisons show that both the algorithms and the manual process contain inaccuracies that are intrinsic in the freedom and unpredictability of Wikipedia growth. Nevertheless, a precise description of the involved issues allows to make informed decisions and to base empirical findings on reproducible evidence. Our goal is to lay the foundation for a solid computational sociology of wikis. For this reason we release the scripts encoding our algorithms as open source and also some datasets extracted out of Wikipedia conversations, in order to let other researchers replicate and improve our initial effort.
Prior scholarship on Wikipedia's collaboration processes has examined the properties of either editors or articles, but not the interactions between both. We analyze the coauthorship network of Wikipedia articles about breaking news demanding intense coordination and compare the properties of these articles and the editors who contribute to them to articles about contemporary and historical events. Using p*/ERGM methods to test a multi-level, multi-theoretical model, we identify how editors' attributes and editing patterns interact with articles' attributes and authorship history. Editors' attributes like prior experience have a stronger influence on collaboration patterns, but article attributes also play significant roles. Finally, we discuss the implications our findings and methods have for understanding the socio-material duality of collective intelligence systems beyond Wikipedia.
A method for measuring co-authorship relationships in mediawiki
Proceedings of the 4th …, 2008
Collaborative writing through wikis has become increasingly popular in recent years. When users contribute to a wiki article they implicitly establish a co-authorship relationship. Discovering these relationships can be of value, for example in finding experts on a given topic. However, it is not trivial to determine the main co-authors for a given author among the potentially thousands who have contributed to a given author's edit history. We have developed a method and algorithm for calculating a co-authorship degree for a given pair of authors. We have implemented this method as an extension for the MediaWiki system and demonstrate its performance which is satisfactory in the majority of cases. This paper also presents a method of determining an expertise group for a chosen topic.
Recognizing Contribution in Wikis: Authorship Categories, Algorithms, and Visualizations
Wikis are designed to support collaborative editing, without focusing on individual contribution, such that it is not straightforward to determine who contributed to a specific page. However, as wikis are increasingly adopted in settings such as business, government, and education, where editors are largely driven by career goals, there is a perceived need to modify wikis so that each editor's contributions are clearly presented. In this paper we introduce an approach for assessing the contributions of wiki editors along several authorship categories, as well as a variety of information glyphs for visualizing this information. We report on three types of analysis: (a) assessing the accuracy of the algorithms, (b) estimating the understandability of the visualizations, and (c) exploring wiki editors' perceptions regarding the extent to which such an approach is likely to change their behavior. Our findings demonstrate that our proposed automated techniques can estimate fairly accurately the quantity of editors' contributions across various authorship categories, and that the visualizations we introduced can clearly convey this information to users. Moreover, our user study suggests that such tools are likely to change wiki editors' behavior. We discuss both the potential benefits and risks associated with solutions for estimating and visualizing wiki contributions.