# Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017

arXiv: https://arxiv.org/abs/1712.06921 · HTML: https://mlsanity.com/paper/1712.06921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 1 of 1)

- [Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection](https://mlsanity.com/paper/2505.18136/) (2025)

## Abstract (arXiv)

The WSDM Cup 2017 is a binary classification task for classifying Wikidata revisions into vandalism and non-vandalism. This paper describes our method using some machine learning techniques such as under-sampling, feature selection, stacking and ensembles of models. We confirm the validity of each technique by calculating AUC-ROC of models using such techniques and not using them. Additionally, we analyze the results and gain useful insights into improving models for the vandalism detection task. The AUC-ROC of our final submission after the deadline resulted in 0.94412.