Difference between revisions of "Case Study: Integrity of Wikipedia Articles"

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{{Infobox work
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| title = Case Study: Integrity of Wikipedia Articles
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| date = 2014
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| authors = [[Amin Ranj Bar]]<br />[[Muthucumaru Maheswaran]]
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| doi = 10.1007/978-3-319-02717-3_6
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| link = https://link.springer.com/chapter/10.1007/978-3-319-02717-3_6/fulltext.html
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}}
 
'''Case Study: Integrity of Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Amin Ranj Bar]] and [[Muthucumaru Maheswaran]].
 
'''Case Study: Integrity of Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Amin Ranj Bar]] and [[Muthucumaru Maheswaran]].
  
 
== Overview ==
 
== Overview ==
 
In this chapter, authors investigate the integrity of [[Wikipedia]] articles as an example of integrity management in crowdsourcing systems. Authors first challenge its integrity by performing some experiments. Afterwards, authors analyze the dump datasets from its website to find the reasons behind high integrity for few articles and low integrity for majority of them.
 
In this chapter, authors investigate the integrity of [[Wikipedia]] articles as an example of integrity management in crowdsourcing systems. Authors first challenge its integrity by performing some experiments. Afterwards, authors analyze the dump datasets from its website to find the reasons behind high integrity for few articles and low integrity for majority of them.

Latest revision as of 09:49, 12 August 2019


Case Study: Integrity of Wikipedia Articles
Authors
Amin Ranj Bar
Muthucumaru Maheswaran
Publication date
2014
DOI
10.1007/978-3-319-02717-3_6
Links
Original

Case Study: Integrity of Wikipedia Articles - scientific work related to Wikipedia quality published in 2014, written by Amin Ranj Bar and Muthucumaru Maheswaran.

Overview

In this chapter, authors investigate the integrity of Wikipedia articles as an example of integrity management in crowdsourcing systems. Authors first challenge its integrity by performing some experiments. Afterwards, authors analyze the dump datasets from its website to find the reasons behind high integrity for few articles and low integrity for majority of them.