# Building automated vandalism detection tools for Wikidata

arXiv: https://arxiv.org/abs/1703.03861 · HTML: https://mlsanity.com/paper/1703.03861/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly](https://mlsanity.com/paper/2405.18845/) (2024)
- [Interpretable classification of wiki-review streams](https://mlsanity.com/paper/2405.18335/) (2024)
- [Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection](https://mlsanity.com/paper/2505.18136/) (2025)
- [Quantifying Collusion Among Autonomous LLM Agents: A Statistical Analysis of the Collusion Wiki Incident](https://mlsanity.com/paper/2610.04528/) (2026)

## Abstract (arXiv)

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.