Building automated vandalism detection tools for Wikidata
Abstract
Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open collaboration model is powerful in that it reduces barriers to participation and allows a large number of people to contribute. However, it exposes the knowledge base to the risk of vandalism and low-quality contributions. In this work, we build on past work detecting vandalism in Wikipedia to detect vandalism in Wikidata. This work is novel in that identifying damaging changes in a structured knowledge-base requires substantially different feature engineering work than in a text-based wiki like Wikipedia. We also discuss the utility of these classifiers for reducing the overall workload of vandalism patrollers in Wikidata. We describe a machine classification strategy that is able to catch 89% of vandalism while reducing patrollers' workload by 98%, by drawing lightly from contextual features of an edit and heavily from the characteristics of the user making the edit.
Keywords
Cite
@article{arxiv.1703.03861,
title = {Building automated vandalism detection tools for Wikidata},
author = {Amir Sarabadani and Aaron Halfaker and Dario Taraborelli},
journal= {arXiv preprint arXiv:1703.03861},
year = {2017}
}