Recognizing contributions in wikis: authorship categories, algorithms, and visualizations

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Recognizing contributions in wikis: authorship categories, algorithms, and visualizations
Authors: Ofer Arazy, Eleni Stroulia, Stan Ruecker, Cristina Arias, Carlos Fiorentino, Veselin Ganev, Timothy Yau [edit item]
Citation: Journal of the American Society for Information Science and Technology 61 (6): 1166-1179. 2010 June.
Publication type: Journal article
Peer-reviewed: Yes
Database(s):
DOI: 10.1002/asi.21326/abstract.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Recognizing contributions in wikis: authorship categories, algorithms, and visualizations is a publication by Ofer Arazy, Eleni Stroulia, Stan Ruecker, Cristina Arias, Carlos Fiorentino, Veselin Ganev, Timothy Yau.


[edit] Abstract

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.

[edit] Research questions

"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."

Research details

Topics: Reputation systems [edit item]
Domains: Computer science, Information science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: Experiment responses, Interview responses, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: User [edit item]
Wikipedia data extraction: Live Wikipedia [edit item]
Wikipedia page type: Article, History [edit item]
Wikipedia language: English [edit item]

[edit] Conclusion

"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."

[edit] Comments

"Wiki features"


Further notes[edit]

Facts about "Recognizing contributions in wikis: authorship categories, algorithms, and visualizations"RDF feed
AbstractWikis are designed to support collaborativWikis 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.mating and visualizing wiki contributions.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
CommentsWiki features
ConclusionOur findings demonstrate that our proposedOur 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.e likely to change wiki editors' behavior.
Data sourceExperiment responses +, Interview responses + and Wikipedia pages +
Doi10.1002/asi.21326/abstract +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Recognizing%2Bcontributions%2Bin%2Bwikis%3A%2Bauthorship%2Bcategories%2C%2Balgorithms%2C%2Band%2Bvisualizations%22 +
Has authorOfer Arazy +, Eleni Stroulia +, Stan Ruecker +, Cristina Arias +, Carlos Fiorentino +, Veselin Ganev + and Timothy Yau +
Has domainComputer science + and Information science +
Has topicReputation systems +
Issue6 +
MonthJune +
Pages1166-1179 +
Peer reviewedYes +
Publication typeJournal article +
Published inJournal of the American Society for Information Science and Technology +
Research designExperiment +
Research questionsIn this paper we introduce an approach forIn 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.proach is likely to change their behavior.
Revid10,923 +
TheoriesUndetermined
Theory typeDesign and action +
TitleRecognizing contributions in wikis: authorship categories, algorithms, and visualizations
Unit of analysisUser +
Urlhttp://onlinelibrary.wiley.com/doi/10.1002/asi.21326/abstract +
Volume61 +
Wikipedia coverageSample data +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageEnglish +
Wikipedia page typeArticle + and History +
Year2010 +