Martin Harrigan

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Martin Harrigan 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
Classifying Wikipedia Articles Using Network Motif Counts and Ratios Quality
Edit Networks
WikiSym English August 2012 Because the production of Wikipedia articles is a collaborative process, the edit network around a article can tell us something about the quality of that article. Articles that have received little attention will have sparse networks; at the other end of the spectrum, articles that are Wikipedia battle grounds will have very crowded networks. In this paper we evaluate the idea of characterizing edit networks as a vector of motif counts that can be used in clustering and classification. Our objective is not immediately to develop a powerful classifier but to assess what is the signal in network motifs. We show that this motif count vector representation is effective for classifying articles on the Wikipedia quality scale. We further show that ratios of motif counts can effectively overcome normalization problems when comparing networks of radically different sizes. 0 0
Classifying Wikipedia articles using network motif counts and ratios Edit networks
Quality
WikiSym 2012 English 2012 Because the production of Wikipedia articles is a collaborative process, the edit network around a article can tell us something about the quality of that article. Articles that have received little attention will have sparse networks; at the other end of the spectrum, articles that are Wikipedia battle grounds will have very crowded networks. In this paper we evaluate the idea of characterizing edit networks as a vector of motif counts that can be used in clustering and classification. Our objective is not immediately to develop a powerful classifier but to assess what is the signal in network motifs. We show that this motif count vector representation is effective for classifying articles on the Wikipedia quality scale. We further show that ratios of motif counts can effectively overcome normalization problems when comparing networks of radically different sizes. 0 0
A Characterization of Wikipedia Content Based on Motifs in the Edit Graph SMUC '11: Proceedings of the 3rd international workshop on Search and mining user-generated contents English February 2011 Good Wikipedia articles are authoritative sources due to the collaboration of a number of knowledgeable contributors. This is the many eyes idea. The edit network associated with a Wikipedia article can tell us something about its quality or authoritativeness. In this paper we explore the hypothesis that the characteristics of this edit network are predictive of the quality of the corresponding article's content. We characterize the edit network using a profile of network motifs and we show that this network motif profile is predictive of the Wikipedia quality classes assigned to articles by Wikipedia editors. We further show that the network motif profile can identify outlier articles particularly in the 'Featured Article' class, the highest Wikipedia quality class. 8 0
Characterizing Wikipedia pages using edit network motif profiles Authoritativeness
Network motifs
Wikipedia
SMUC English 2011 Good Wikipedia articles are authoritative sources due to the collaboration of a number of knowledgeable contributors. This is the many eyes idea. The edit network associated with a Wikipedia article can tell us something about its quality or authoritativeness. In this paper we explore the hypothesis that the characteristics of this edit network are predictive of the quality of the corresponding article's content. We characterize the edit network using a profile of network motifs and we show that this network motif profile is predictive of the Wikipedia quality classes assigned to articles by Wikipedia editors. We further show that the network motif profile can identify outlier articles particularly in the 'Featured Article' class, the highest Wikipedia quality class. 0 0