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- W4379474684 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." @default.
- W4379474684 created "2023-06-07" @default.
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- W4379474684 date "2023-06-02" @default.
- W4379474684 modified "2023-10-01" @default.
- W4379474684 title "Fair multilingual vandalism detection system for Wikipedia" @default.
- W4379474684 doi "https://doi.org/10.48550/arxiv.2306.01650" @default.
- W4379474684 hasPublicationYear "2023" @default.
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