A method for measuring co-authorship relationships in mediawiki (original) (raw)
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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.
Proceedings of the 22nd ACM conference on Hypertext and hypermedia - HT '11, 2011
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.
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.
Recognizing contributions in wikis: Authorship categories, algorithms, and visualizations
Journal of the …, 2010
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.
MEASURING AUTHOR CONTRIBUTIONS TO THE MEDIAWIKI
npo-consulting.net
Wiki is a one of the most effective collaborative authoring tool which comes with Web 2.0. They bring about new vision to group work and help to take place in online learning environments rapidly. Although there is a great potential for learning environments, ...
Dandelion: supporting coordinated, collaborative authoring in Wikis
2010
Dandelion is a tool that extends wikis to support coordinated, collaborative authoring using a tag-based approach. Specifically, users can insert tags in a wiki page to specify various co-authoring tasks. These tags can then be executed to help drive and manage the collaboration workflow, and provide content-centric collaboration awareness for all the co-authors. Four successful pilot deployments and positive user feedback show the practical value of Dandelion, especially its value in supporting a structured, collaborative authoring process often seen in business settings.
What Makes a Good Collaborative Knowledge Graph: Group Composition and Quality in Wikidata
Wikidata is a community-driven knowledge graph which has drawn much attention from researchers and practitioners since its inception in 2012. The large user pool behind this project has been able to produce information spanning over several domains, which is openly released and can be reused to feed any information-based application. Collaborative production processes in Wikidata have not yet been explored. Understanding them is key to prevent potentially harmful community dynamics and ensure the sustainability of the project in the long run. We performed a regression analysis to investigate how the contribution of different types of users, i.e. bots and human editors, registered or anonymous, influences outcome quality in Wikidata. Moreover, we looked at the effects of tenure and interest diversity among registered users. Our findings show that a balanced contribution of bots and human editors positively influence outcome quality, whereas higher numbers of anonymous edits may hinder performance. Tenure and interest diversity within groups also lead to higher quality. These results may be helpful to identify and address groups that are likely to underperform in Wikidata. Further work should analyse in detail the respective contributions of bots and registered users.
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.
In this work, we present a novel approach to detecting authorship of words in Wikipedia, which outperforms the baseline method in terms of accuracy. This is achieved by reducing the necessary word-based text-to-text comparisons, which are the most fallible steps in the process. To provide an aggregated measure of the concentration, we calculate a gini coefficient for each revision of an article based on our word-author-assignments. As a motivation for calculating this measure we argue that the concentration of words to just a few authors can be an indicator for a lack of quality and neutrality in an article. The coefficient development over time in an article is visualized and provided online as an easily accessible and useful tool to investigate how the content of an article evolved. We present examples where the gini curve gives useful insights into differences of articles and may help to spot crucial events in the past evolution of an article.