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.

本文介绍了一种新型系统的设计，旨在支持维基百科社区解决平台上的破坏行为。通过收集 47 种语言的大规模数据集和应用高级过滤和特征工程技术，包括多语言掩蔽语言模型，从人类生成的数据中构建训练数据集。通过与维基百科生产中使用的 ORES 进行比较评估系统的性能。我们的研究结果大大增加了覆盖的语言数量，使维基百科的巡逻对更广泛的社区更加高效。此外，我们的模型胜过 ORES，确保所提供的结果不仅更准确，而且对某些贡献者群体的偏见较小。