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This paper presents a novel design of the system aimed at supporting theWikipedia community in addressing vandalism on the platform. To achieve this,we collected a massive dataset of 47 languages, and applied advanced filteringand feature engineering techniques, including multilingual masked languagemodeling to build the training dataset from human-generated data. Theperformance of the system was evaluated through comparison with the one used inproduction in Wikipedia, known as ORES. Our research results in a significantincrease in the number of languages covered, making Wikipedia patrolling moreefficient to a wider range of communities. Furthermore, our model outperformsORES, ensuring that the results provided are not only more accurate but alsoless biased against certain groups of contributors.

Fair multilingual vandalism detection system for Wikipedia