Difference between revisions of "Will They Stay or Will They Go? How Network Properties of Webics Predict Dropout Rates of Valuable Wikipedians"
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| + | {{Infobox work | ||
| + | | title = Will They Stay or Will They Go? How Network Properties of Webics Predict Dropout Rates of Valuable Wikipedians | ||
| + | | date = 2011 | ||
| + | | authors = [[Jürgen Lerner]]<br />[[Patrick Kenis]]<br />[[Denise van Raaij]]<br />[[Ulrik Brandes]] | ||
| + | | doi = 10.1016/j.emj.2011.02.003 | ||
| + | | link = http://www.sciencedirect.com/science/article/pii/S0263237311000053 | ||
| + | }} | ||
'''Will They Stay or Will They Go? How Network Properties of Webics Predict Dropout Rates of Valuable Wikipedians''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Jürgen Lerner]], [[Patrick Kenis]], [[Denise van Raaij]] and [[Ulrik Brandes]]. | '''Will They Stay or Will They Go? How Network Properties of Webics Predict Dropout Rates of Valuable Wikipedians''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Jürgen Lerner]], [[Patrick Kenis]], [[Denise van Raaij]] and [[Ulrik Brandes]]. | ||
== Overview == | == Overview == | ||
This paper contributes to understanding of an increasingly prevalent work system, web-based internet communities (WebICs). Authors are particularly interested in how WebICs are governed given the fact how different they are compared to more classical forms of organization. Authors study the governance of a WebIC by studying the structure and dynamics of their edit network. Given the fact that the edit network is a relational structure, [[social network]] analysis is key to understanding these work systems. Authors demonstrate that characteristics of the edit network contribute to predicting the dropout hazard of valuable WebIC members. Since WebICs exist only thanks to the activity of their contributors, predicting drop-outs becomes crucial. The results show that [[reputation]] and controversy have different effects for different types of [[Wikipedia]]ns; i.e., an actor’s reputation decreases the dropout hazard of active [[Wikipedians]], while participation on controversial pages decreases the dropout hazard of highly active Wikipedians. | This paper contributes to understanding of an increasingly prevalent work system, web-based internet communities (WebICs). Authors are particularly interested in how WebICs are governed given the fact how different they are compared to more classical forms of organization. Authors study the governance of a WebIC by studying the structure and dynamics of their edit network. Given the fact that the edit network is a relational structure, [[social network]] analysis is key to understanding these work systems. Authors demonstrate that characteristics of the edit network contribute to predicting the dropout hazard of valuable WebIC members. Since WebICs exist only thanks to the activity of their contributors, predicting drop-outs becomes crucial. The results show that [[reputation]] and controversy have different effects for different types of [[Wikipedia]]ns; i.e., an actor’s reputation decreases the dropout hazard of active [[Wikipedians]], while participation on controversial pages decreases the dropout hazard of highly active Wikipedians. | ||
Revision as of 11:16, 19 December 2019
Authors | Jürgen Lerner Patrick Kenis Denise van Raaij Ulrik Brandes |
|---|---|
Publication date | 2011 |
DOI | 10.1016/j.emj.2011.02.003 |
Links | Original |
Will They Stay or Will They Go? How Network Properties of Webics Predict Dropout Rates of Valuable Wikipedians - scientific work related to Wikipedia quality published in 2011, written by Jürgen Lerner, Patrick Kenis, Denise van Raaij and Ulrik Brandes.
Overview
This paper contributes to understanding of an increasingly prevalent work system, web-based internet communities (WebICs). Authors are particularly interested in how WebICs are governed given the fact how different they are compared to more classical forms of organization. Authors study the governance of a WebIC by studying the structure and dynamics of their edit network. Given the fact that the edit network is a relational structure, social network analysis is key to understanding these work systems. Authors demonstrate that characteristics of the edit network contribute to predicting the dropout hazard of valuable WebIC members. Since WebICs exist only thanks to the activity of their contributors, predicting drop-outs becomes crucial. The results show that reputation and controversy have different effects for different types of Wikipedians; i.e., an actor’s reputation decreases the dropout hazard of active Wikipedians, while participation on controversial pages decreases the dropout hazard of highly active Wikipedians.
