@inproceedings{raszewski-de-kock-2025-detecting,
title = "Detecting Sockpuppetry on {W}ikipedia Using Meta-Learning",
author = "Raszewski, Luc and
de Kock, Christine",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1083/",
doi = "10.18653/v1/2025.acl-long.1083",
pages = "22252--22264",
ISBN = "979-8-89176-251-0",
abstract = "Malicious sockpuppet detection on Wikipedia is critical to preserving access to reliable information on the internet and preventing the spread of disinformation. Prior machine learning approaches rely on stylistic and meta-data features, but do not prioritise adaptability to author-specific behaviours. As a result, they struggle to effectively model the behaviour of specific sockpuppet-groups, especially when text data is limited. To address this, we propose the application of meta-learning, a machine learning technique designed to improve performance in data-scarce settings by training models across multiple tasks. Meta-learning optimises a model for rapid adaptation to the writing style of a new sockpuppet-group. Our results show that meta-learning significantly enhances the precision of predictions compared to pre-trained models, marking an advancement in combating sockpuppetry on open editing platforms. We release an updated dataset of sockpuppet investigations to foster future research in both sockpuppetry and meta-learning fields."
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<abstract>Malicious sockpuppet detection on Wikipedia is critical to preserving access to reliable information on the internet and preventing the spread of disinformation. Prior machine learning approaches rely on stylistic and meta-data features, but do not prioritise adaptability to author-specific behaviours. As a result, they struggle to effectively model the behaviour of specific sockpuppet-groups, especially when text data is limited. To address this, we propose the application of meta-learning, a machine learning technique designed to improve performance in data-scarce settings by training models across multiple tasks. Meta-learning optimises a model for rapid adaptation to the writing style of a new sockpuppet-group. Our results show that meta-learning significantly enhances the precision of predictions compared to pre-trained models, marking an advancement in combating sockpuppetry on open editing platforms. We release an updated dataset of sockpuppet investigations to foster future research in both sockpuppetry and meta-learning fields.</abstract>
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%0 Conference Proceedings
%T Detecting Sockpuppetry on Wikipedia Using Meta-Learning
%A Raszewski, Luc
%A de Kock, Christine
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F raszewski-de-kock-2025-detecting
%X Malicious sockpuppet detection on Wikipedia is critical to preserving access to reliable information on the internet and preventing the spread of disinformation. Prior machine learning approaches rely on stylistic and meta-data features, but do not prioritise adaptability to author-specific behaviours. As a result, they struggle to effectively model the behaviour of specific sockpuppet-groups, especially when text data is limited. To address this, we propose the application of meta-learning, a machine learning technique designed to improve performance in data-scarce settings by training models across multiple tasks. Meta-learning optimises a model for rapid adaptation to the writing style of a new sockpuppet-group. Our results show that meta-learning significantly enhances the precision of predictions compared to pre-trained models, marking an advancement in combating sockpuppetry on open editing platforms. We release an updated dataset of sockpuppet investigations to foster future research in both sockpuppetry and meta-learning fields.
%R 10.18653/v1/2025.acl-long.1083
%U https://aclanthology.org/2025.acl-long.1083/
%U https://doi.org/10.18653/v1/2025.acl-long.1083
%P 22252-22264
Markdown (Informal)
[Detecting Sockpuppetry on Wikipedia Using Meta-Learning](https://aclanthology.org/2025.acl-long.1083/) (Raszewski & de Kock, ACL 2025)
ACL
- Luc Raszewski and Christine de Kock. 2025. Detecting Sockpuppetry on Wikipedia Using Meta-Learning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22252–22264, Vienna, Austria. Association for Computational Linguistics.