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A content-context-centric approach for detecting vandalism in Wikipedia
Abstract Collaborative online social media (CSM) apCollaborative online social media (CSM) applications such as Wikipedia have not only revolutionized the World Wide Web, but they also have had a hugely positive effect on modern free societies. Unfortunately, Wikipedia has also become target to a wide-variety of vandalism attacks. Most existing vandalism detection techniques rely upon simple textual features such as existence of abusive language or spammy words. These techniques are ineffective against sophisticated vandal edits, which often do not contain the tell-tale markers associated with vandalism. In this paper, we argue for a context-aware approach for vandalism detection. This paper proposes a content-context-aware vandalism detection framework. The main idea is to quantify how well the words contained in the edit fit into the topic and the existing content of the Wikipedia article. We present two novel metrics, called WWW co-occurrence probability and top-ranked co-occurrence probability for this purpose. We also develop efficient mechanisms for evaluating these two metrics, and machine learning-based schemes that utilize these metrics. The paper presents a range of experiments to demonstrate the effectiveness of the proposed approach.he effectiveness of the proposed approach.
Abstractsub Collaborative online social media (CSM) apCollaborative online social media (CSM) applications such as Wikipedia have not only revolutionized the World Wide Web, but they also have had a hugely positive effect on modern free societies. Unfortunately, Wikipedia has also become target to a wide-variety of vandalism attacks. Most existing vandalism detection techniques rely upon simple textual features such as existence of abusive language or spammy words. These techniques are ineffective against sophisticated vandal edits, which often do not contain the tell-tale markers associated with vandalism. In this paper, we argue for a context-aware approach for vandalism detection. This paper proposes a content-context-aware vandalism detection framework. The main idea is to quantify how well the words contained in the edit fit into the topic and the existing content of the Wikipedia article. We present two novel metrics, called WWW co-occurrence probability and top-ranked co-occurrence probability for this purpose. We also develop efficient mechanisms for evaluating these two metrics, and machine learning-based schemes that utilize these metrics. The paper presents a range of experiments to demonstrate the effectiveness of the proposed approach.he effectiveness of the proposed approach.
Bibtextype inproceedings  +
Doi 10.4108/icst.collaboratecom.2013.254059  +
Has author Lakshmish Ramaswamy + , Tummalapenta R.S. + , Li K. + , Calton Pu +
Has extra keyword Co-occurrence probability + , Content-context + , Context-aware approaches + , Detection framework + , Online social medias + , Textual features + , Wikipedia + , Wikipedia articles + , Social networking (online) + , World Wide Web + , Probability +
Has keyword Collaborative online social media + , Content-context + , Top-ranked co-occurrence probability + , Vandalism detection + , WWW co-occurrence probability +
Isbn 9781936968923  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 115–122  +
Published in Proceedings of the 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, COLLABORATECOM 2013 +
Title A content-context-centric approach for detecting vandalism in Wikipedia +
Type conference paper  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 6 November 2014 14:12:46  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 14:12:46  +
DateThis property is a special property in this wiki. 2013  +
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