Fair Multilingual Vandalism Detection System for Wikipedia

  1. Mykola Trokhymovych, Muniza Aslam, Ai-Jou Chou, Ricardo Baeza-Yates, Diego Saez-Trumper
    Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
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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.

Citation

@inproceedings{10.1145/3580305.3599823,
  author = {Trokhymovych, Mykola and Aslam, Muniza and Chou, Ai-Jou and Baeza-Yates, Ricardo and Saez-Trumper, Diego},
  title = {Fair Multilingual Vandalism Detection System for Wikipedia},
  year = {2023},
  isbn = {9798400701030},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3580305.3599823},
  doi = {10.1145/3580305.3599823},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {4981--4990},
  numpages = {10},
  location = {Long Beach, CA, USA},
  series = {KDD '23},
}

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