Detecting Sockpuppetry on Wikipedia Using Meta-Learning

ACL ARR 2025 February Submission6498 Authors

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.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: meta learning, contrastive learning, word embeddings, few-shot learning, style analysis

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings, Data resources

Languages Studied: English

Submission Number: 6498