Morten Warncke-Wang

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Morten Warncke-Wang is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Tell me more: An actionable quality model for wikipedia Classication
Flaw detection
Information quality
Machine learning
Modelling
Wikipedia
Proceedings of the 9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013 English 2013 In this paper we address the problem of developing actionable quality models for Wikipedia, models whose features directly suggest strategies for improving the quality of a given article. We rst survey the literature in order to understand the notion of article quality in the context of Wikipedia and existing approaches to automatically assess article quality. We then develop classication models with varying combinations of more or less actionable features, and nd that a model that only contains clearly actionable features delivers solid performance. Lastly we discuss the implications of these results in terms of how they can help improve the quality of articles across Wikipedia. Categories and Subject Descriptors H.5 [Information Interfaces and Presentation]: Group and Organization InterfacesCollaborative computing, Computer-supported cooperative work, Web-based interac- Tion. Copyright 2010 ACM. 0 0
In Search of the Ur-Wikipedia: Universality, Similarity, and Translation in the Wikipedia Inter-Language Link Network Wikipedia
Tobler's Law
First Law of Geography
WikiSym English August 2012 Wikipedia has become one of the primary encyclopaedic information repositories on the World Wide Web. It started in 2001 with a single edition in the English language and has since expanded to more than 20 million articles in 283 languages. Criss-crossing between the Wikipedias is an interlanguage link network, connecting the articles of one edition of Wikipedia to another. We describe characteristics of articles covered by nearly all Wikipedias and those covered by only a single language edition, we use the network to understand how we can judge the similarity between Wikipedias based on concept coverage, and we investigate the flow of translation between a selection of the larger Wikipedias. Our findings indicate that the relationships between Wikipedia editions follow Tobler's first law of geography: similarity decreases with increasing distance. The number of articles in a Wikipedia edition is found to be the strongest predictor of similarity, while language similarity also appears to have an influence. The English Wikipedia edition is by far the primary source of translations. We discuss the impact of these results for Wikipedia as well as user-generated content communities in general. 0 0
In search of the ur-Wikipedia: Universality, similarity, and translation in the Wikipedia inter-language link network First law of geography
Tobler's law
Wikipedia
WikiSym 2012 English 2012 Wikipedia has become one of the primary encyclopaedic information repositories on the World Wide Web. It started in 2001 with a single edition in the English language and has since expanded to more than 20 million articles in 283 languages. Criss-crossing between the Wikipedias is an inter-language link network, connecting the articles of one edition of Wikipedia to another. We describe characteristics of articles covered by nearly all Wikipedias and those covered by only a single language edition, we use the network to understand how we can judge the similarity between Wikipedias based on concept coverage, and we investigate the flow of translation between a selection of the larger Wikipedias. Our findings indicate that the relationships between Wikipedia editions follow Tobler's first law of geography: similarity decreases with increasing distance. The number of articles in a Wikipedia edition is found to be the strongest predictor of similarity, while language similarity also appears to have an influence. The English Wikipedia edition is by far the primary source of translations. We discuss the impact of these results for Wikipedia as well as user-generated content communities in general. 0 0