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Gueorgi Kossinets

 

Publications by Gueorgi Kossinets (bibliography)

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2011
 
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Welser, Howard T., Cosley, Dan, Kossinets, Gueorgi, Lin, Austin, Dokshin, Fedor, Gay, Geri and Smith, Marc (2011): Finding social roles in Wikipedia. In: Proceedings of the 2011 iConference 2011. pp. 122-129. Available online

This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.

© All rights reserved Welser et al. and/or ACM Press

2009
 
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Danescu-Niculescu-Mizil, Cristian, Kossinets, Gueorgi, Kleinberg, Jon and Lee, Lillian (2009): How opinions are received by online communities: a case study on amazon.com helpfulness votes. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 141-150. Available online

There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries.

© All rights reserved Danescu-Niculescu-Mizil et al. and/or ACM Press

 
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