Fair multilingual vandalism detection system for Wikipedia
Abstract
This paper presents a novel design of the system aimed at supporting the Wikipedia community in addressing vandalism on the platform. To achieve this, we collected a massive dataset of 47 languages, and applied advanced filtering and feature engineering techniques, including multilingual masked language modeling to build the training dataset from human-generated data. The performance of the system was evaluated through comparison with the one used in production in Wikipedia, known as ORES. Our research results in a significant increase in the number of languages covered, making Wikipedia patrolling more efficient to a wider range of communities. Furthermore, our model outperforms ORES, ensuring that the results provided are not only more accurate but also less biased against certain groups of contributors.
Keywords
Cite
@article{arxiv.2306.01650,
title = {Fair multilingual vandalism detection system for Wikipedia},
author = {Mykola Trokhymovych and Muniza Aslam and Ai-Jou Chou and Ricardo Baeza-Yates and Diego Saez-Trumper},
journal= {arXiv preprint arXiv:2306.01650},
year = {2023}
}