Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling
Authors | William Yang Wang Kathleen R. McKeown |
|---|---|
Publication date | 2010 |
DOI | 10.7916/D8KP89J4 |
Links | Original |
Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling - scientific work related to Wikipedia quality published in 2010, written by William Yang Wang and Kathleen R. McKeown.
Overview
Discriminating vandalism edits from non-vandalism edits in Wikipedia is a challenging task, as ill-intentioned edits can include a variety of content and be expressed in many different forms and styles. Previous studies are limited to rule-based methods and learning based on lexical features, lacking in linguistic analysis. In this paper, authors propose a novel Web-based shallow syntactic-semantic modeling method, which utilizes Web search results as resource and trains topic-specific n-tag and syntactic n-gram language models to detect vandalism. By combining basic task-specific and lexical features, authors have achieved high F-measures using logistic boosting and logistic model trees classifiers, surpassing the results reported by major Wikipedia vandalism detection systems.
