Paper

Building automated vandalism detection tools for Wikidata

Sarabadani, Amir, Taraborelli, Dario, Halfaker, Aaron

2017

Overview

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.

• Publisher: Cornell University Library, arXiv.org
• Subject: Damage detection / Knowledge base / Structural damage / Vandalism / Workload / Workloads