Lakshmish Ramaswamy

From WikiPapers
Jump to: navigation, search

Lakshmish Ramaswamy is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
A content-context-centric approach for detecting vandalism in Wikipedia Collaborative online social media
Content-context
Top-ranked co-occurrence probability
Vandalism detection
WWW co-occurrence probability
Proceedings of the 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, COLLABORATECOM 2013 English 2013 Collaborative 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. 0 0
Elusive vandalism detection in Wikipedia: A text stability-based approach Classification
Vandalism detection
Wiki
International Conference on Information and Knowledge Management, Proceedings English 2010 The open collaborative nature of wikis encourages participation of all users, but at the same time exposes their content to vandalism. The current vandalism-detection techniques, while effective against relatively obvious vandalism edits, prove to be inadequate in detecting increasingly prevalent sophisticated (or elusive) vandal edits. We identify a number of vandal edits that can take hours, even days, to correct and propose a text stability-based approach for detecting them. Our approach is focused on the likelihood of a certain part of an article being modified by a regular edit. In addition to text-stability, our machine learning-based technique also takes into account edit patterns. We evaluate the performance of our approach on a corpus comprising of 15000 manually labeled edits from the Wikipedia Vandalism PAN corpus. The experimental results show that text-stability is able to improve the performance of the selected machine-learning algorithms significantly. 0 0
Elusive vandalism detection in wikipedia: a text stability-based approach Classification
Vandalism detection
Wiki
CIKM English 2010 0 0