A single multilingual language model serializes all Wikidata edits into text and outperforms the production ORES system in vandalism detection and fairness.
Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017
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abstract
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.
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Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detection
A single multilingual language model serializes all Wikidata edits into text and outperforms the production ORES system in vandalism detection and fairness.