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 a new dataset of sockpuppet investigations to foster future research in both sockpuppetry and meta-learning fields.