Difference between revisions of "Detecting Wikipedia Vandalism Using Machine Learning - Notebook for Pan at Clef 2011"
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| + | | title = Detecting Wikipedia Vandalism Using Machine Learning - Notebook for Pan at Clef 2011 | ||
| + | | date = 2011 | ||
| + | | authors = [[Cristian-Alexandru Dragusanu]]<br />[[Marina Cufliuc]]<br />[[Adrian Iftene]] | ||
| + | | link = http://ceur-ws.org/Vol-1177/CLEF2011wn-PAN-DragusanuEt2011.pdf | ||
| + | }} | ||
'''Detecting Wikipedia Vandalism Using Machine Learning - Notebook for Pan at Clef 2011''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Cristian-Alexandru Dragusanu]], [[Marina Cufliuc]] and [[Adrian Iftene]]. | '''Detecting Wikipedia Vandalism Using Machine Learning - Notebook for Pan at Clef 2011''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Cristian-Alexandru Dragusanu]], [[Marina Cufliuc]] and [[Adrian Iftene]]. | ||
== Overview == | == Overview == | ||
Wikipedia vandalism identification is a very complex issue, which is now mostly solved manually by volunteers. This paper presents the main components of a system built by group in order to automatically identify vandalized [[Wikipedia]] articles. The main component of system is a machine learning component that uses three types of [[features]] grouped in 3 classes: Metadata, Text and Language. Additional to previous approaches authors consider 4 new features related to vulgar, biased, sexual and miscellaneous bad words. The obtained results showed an area of 0.42464 under the PR-AUC curve and an area of 0.82963 under the ROC-AUC curve. | Wikipedia vandalism identification is a very complex issue, which is now mostly solved manually by volunteers. This paper presents the main components of a system built by group in order to automatically identify vandalized [[Wikipedia]] articles. The main component of system is a machine learning component that uses three types of [[features]] grouped in 3 classes: Metadata, Text and Language. Additional to previous approaches authors consider 4 new features related to vulgar, biased, sexual and miscellaneous bad words. The obtained results showed an area of 0.42464 under the PR-AUC curve and an area of 0.82963 under the ROC-AUC curve. | ||
Revision as of 11:02, 25 January 2020
Authors | Cristian-Alexandru Dragusanu Marina Cufliuc Adrian Iftene |
|---|---|
Publication date | 2011 |
Links | Original |
Detecting Wikipedia Vandalism Using Machine Learning - Notebook for Pan at Clef 2011 - scientific work related to Wikipedia quality published in 2011, written by Cristian-Alexandru Dragusanu, Marina Cufliuc and Adrian Iftene.
Overview
Wikipedia vandalism identification is a very complex issue, which is now mostly solved manually by volunteers. This paper presents the main components of a system built by group in order to automatically identify vandalized Wikipedia articles. The main component of system is a machine learning component that uses three types of features grouped in 3 classes: Metadata, Text and Language. Additional to previous approaches authors consider 4 new features related to vulgar, biased, sexual and miscellaneous bad words. The obtained results showed an area of 0.42464 under the PR-AUC curve and an area of 0.82963 under the ROC-AUC curve.
