Difference between revisions of "Content-Based Conflict of Interest Detection on Wikipedia"
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| + | | title = Content-Based Conflict of Interest Detection on Wikipedia | ||
| + | | date = 2018 | ||
| + | | authors = [[Orizu Udochukwu]]<br />[[Yulan He]] | ||
| + | | link = https://research.aston.ac.uk/portal/en/researchoutput/contentbased-conflictofinterest-detection-on-wikipedia(4d2cd95d-5418-4a8f-bad4-386379e3b105).html | ||
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'''Content-Based Conflict of Interest Detection on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Orizu Udochukwu]] and [[Yulan He]]. | '''Content-Based Conflict of Interest Detection on Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Orizu Udochukwu]] and [[Yulan He]]. | ||
== Overview == | == Overview == | ||
Wikipedia is one of the most visited websites in the world. On [[Wikipedia]], Conflict-of-Interest (CoI) editing happens when an editor uses Wikipedia to advance their interests or relationships. This includes paid editing done by organisations for public relations purposes, etc. CoI detection is highly subjective and though closely related to vandalism and bias detection, it is a more difficult problem. In this paper, authors frame CoI detection as a binary classification problem and explore various [[features]] which can be used to train supervised classifiers for CoI detection on Wikipedia articles. Authors experimental results show that the best F-measure achieved is 0.67 by training SVM from a combination of features including stylometric, bias and emotion features. As authors are not certain that non-CoI set does not contain any CoI articles, authors have also explored the use of one-class classification for CoI detection. The results show that using stylometric features outperforms other types of features or a combination of them and gives an F-measure of 0.63. Also, while binary classifiers give higher recall values (0.81∼0.94), one-class classifier attains higher precision values (0.69∼0.74) | Wikipedia is one of the most visited websites in the world. On [[Wikipedia]], Conflict-of-Interest (CoI) editing happens when an editor uses Wikipedia to advance their interests or relationships. This includes paid editing done by organisations for public relations purposes, etc. CoI detection is highly subjective and though closely related to vandalism and bias detection, it is a more difficult problem. In this paper, authors frame CoI detection as a binary classification problem and explore various [[features]] which can be used to train supervised classifiers for CoI detection on Wikipedia articles. Authors experimental results show that the best F-measure achieved is 0.67 by training SVM from a combination of features including stylometric, bias and emotion features. As authors are not certain that non-CoI set does not contain any CoI articles, authors have also explored the use of one-class classification for CoI detection. The results show that using stylometric features outperforms other types of features or a combination of them and gives an F-measure of 0.63. Also, while binary classifiers give higher recall values (0.81∼0.94), one-class classifier attains higher precision values (0.69∼0.74) | ||
Revision as of 13:18, 21 December 2019
Authors | Orizu Udochukwu Yulan He |
|---|---|
Publication date | 2018 |
Links | Original |
Content-Based Conflict of Interest Detection on Wikipedia - scientific work related to Wikipedia quality published in 2018, written by Orizu Udochukwu and Yulan He.
Overview
Wikipedia is one of the most visited websites in the world. On Wikipedia, Conflict-of-Interest (CoI) editing happens when an editor uses Wikipedia to advance their interests or relationships. This includes paid editing done by organisations for public relations purposes, etc. CoI detection is highly subjective and though closely related to vandalism and bias detection, it is a more difficult problem. In this paper, authors frame CoI detection as a binary classification problem and explore various features which can be used to train supervised classifiers for CoI detection on Wikipedia articles. Authors experimental results show that the best F-measure achieved is 0.67 by training SVM from a combination of features including stylometric, bias and emotion features. As authors are not certain that non-CoI set does not contain any CoI articles, authors have also explored the use of one-class classification for CoI detection. The results show that using stylometric features outperforms other types of features or a combination of them and gives an F-measure of 0.63. Also, while binary classifiers give higher recall values (0.81∼0.94), one-class classifier attains higher precision values (0.69∼0.74)
