State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g.\ the influence campaign by Russia's Internet Research Agency on the 2016 US Election), and they fall short when dealing with {\em novel} campaigns with new targets. We propose MetaTroll, a text-based troll detection model based on the meta-learning framework that enables high portability and parameter-efficient adaptation to new campaigns using only a handful of labelled samples for few-shot transfer. We introduce \textit{campaign-specific} transformer adapters to MetaTroll to ``memorise'' campaign-specific knowledge so as to tackle catastrophic forgetting, where a model ``forgets'' how to detect trolls from older campaigns due to continual adaptation. Our experiments demonstrate that MetaTroll substantially outperforms baselines and state-of-the-art few-shot text classification models. Lastly, we explore simple approaches to extend MetaTroll to multilingual and multimodal detection. Source code for MetaTroll is available at: https://github.com/ltian678/metatroll-code.git.

翻译：国家资助水军是社交媒体上影响力战役的主要实施者，自动检测水军对于大规模打击虚假信息至关重要。现有水军检测模型基于已知战役（例如俄罗斯互联网研究机构针对2016年美国大选的影响力战役）的训练数据开发，当面临具有新目标的**新型**战役时往往表现不佳。我们提出MetaTroll——一种基于元学习框架的文本水军检测模型，该模型具有高可移植性，仅需少量标注样本即可通过参数高效适配应对新战役。我们在MetaTroll中引入**战役特定**Transformer适配器，以“记忆”战役特定知识，从而解决模型因持续适配而“遗忘”旧战役检测能力的灾难性遗忘问题。实验表明，MetaTroll显著优于基线方法和当前最先进的少样本文本分类模型。最后，我们探索了将MetaTroll扩展至多语言和多模态检测的简单方法。MetaTroll源代码已开源：https://github.com/ltian678/metatroll-code.git。