Departmental Papers (CIS)

Document Type

Conference Paper

Date of this Version

9-21-2011

Comments

Andrew G. West and Insup Lee. Multilingual Vandalism Detection using Language-Independent & Ex Post Facto Evidence. In PAN-CLEF '11: Notebook Papers on Uncovering Plagiarism, Authorship, and Social Software Misuse, Amsterdam, the Netherlands. September 2011.

Abstract

There is much literature on Wikipedia vandalism detection. However, this writing addresses two facets given little treatment to date. First, prior efforts emphasize zero-delay detection, classifying edits the moment they are made. If classification can be delayed (e.g., compiling offline distributions), it is possible to leverage ex post facto evidence. This work describes/evaluates several features of this type, which we find to be overwhelmingly strong vandalism indicators.

Second, English Wikipedia has been the primary test-bed for research. Yet, Wikipedia has 200+ language editions and use of localized features impairs portability. This work implements an extensive set of language-independent indicators and evaluates them using three corpora (German, English, Spanish). The work then extends to include language-specific signals. Quantifying their performance benefit, we find that such features can moderately increase classifier accuracy, but significant effort and language fluency are required to capture this utility.

Aside from these novel aspects, this effort also broadly addresses the task, implementing 65 total features. Evaluation produces 0.840 PR-AUC on the zero-delay task and 0.906 PR-AUC with ex post facto evidence (averaging languages). Performance matches the state-of-the-art (English), sets novel baselines (German, Spanish), and is validated by a first-place finish over the 2011 PAN-CLEF test set.

Keywords

Wikipedia, vandalism, collaborative software, collaborative security, social software misuse, feature selection, machine learning

 

Date Posted: 05 October 2011

This document has been peer reviewed.