Difference between revisions of "Measuring Quality of Collaboratively Edited Documents: The Case of Wikipedia"
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| authors = [[Quang-Vinh Dang]]<br />[[Claudia-Lavinia Ignat]] | | authors = [[Quang-Vinh Dang]]<br />[[Claudia-Lavinia Ignat]] | ||
| isbn = 978-150904607-2 | | isbn = 978-150904607-2 | ||
| − | | doi = 10.1109/CIC.2016. | + | | doi = 10.1109/CIC.2016.044 |
| + | | link = https://ieeexplore.ieee.org/document/7809715/ | ||
| + | | plink = https://hal.archives-ouvertes.fr/hal-01388614/document | ||
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'''Measuring Quality of Collaboratively Edited Documents: The Case of Wikipedia''' - scientific work about [[Wikipedia quality]] published in 2017, written by [[Quang-Vinh Dang]] and [[Claudia-Lavinia Ignat]]. | '''Measuring Quality of Collaboratively Edited Documents: The Case of Wikipedia''' - scientific work about [[Wikipedia quality]] published in 2017, written by [[Quang-Vinh Dang]] and [[Claudia-Lavinia Ignat]]. | ||
Revision as of 00:40, 24 June 2018
Authors | Quang-Vinh Dang Claudia-Lavinia Ignat |
|---|---|
Publication date | 2017 |
ISBN | 978-150904607-2 |
DOI | 10.1109/CIC.2016.044 |
Links | Original Preprint |
Measuring Quality of Collaboratively Edited Documents: The Case of Wikipedia - scientific work about Wikipedia quality published in 2017, written by Quang-Vinh Dang and Claudia-Lavinia Ignat.
Overview
Wikipedia is a great example of large scale collaboration, where people from all over the world together build the largest and maybe the most important human knowledge repository in the history. However, a number of studies showed that the quality of Wikipedia articles is not equally distributed. While many articles are of good quality, many others need to be improved. Assessing the quality of Wikipedia articles is very important for guiding readers towards articles of high quality and suggesting authors and reviewers which articles need to be improved. Due to the huge size of Wikipedia, an effective automatic assessment method to measure Wikipedia articles quality is needed. In this paper, authors present an automatic assessment method of Wikipedia articles quality by analyzing their content in terms of their format features and readability scores. Our results show improvements both in terms of accuracy and information gain compared with other existing approaches.
