Fair multilingual vandalism detection system for Wikipedia
This work addresses the need for fair and efficient vandalism detection across diverse Wikipedia communities, representing a domain-specific improvement.
The paper tackles the problem of detecting vandalism on Wikipedia by developing a multilingual system that covers 47 languages and outperforms the existing ORES production system, resulting in more accurate and less biased detection.
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.