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：国家赞助的巨魔是社交媒体影响运动的主要行为者,自动发现巨魔是打击大规模错误信息的重要手段。现有的巨魔探测模型是根据已知运动的培训数据(例如俄罗斯互联网研究机构对2016年美国大选的影响)开发的,在处理具有新目标的“超新颖”运动时没有达到新的目标。我们提议MetatTrall,这是一个基于元学习框架的基于文本的巨魔探测模型,它能够高可移动性和有参数效率地适应新运动,只使用少数标签的样本进行微小的传输。我们引入了Metalit{campaign-aign)变异器,用于MetalTroll到“Mememoris”的特定运动知识,以便解决灾难性的遗忘问题,在那里,我们有一个模型“Forget'如何在持续适应的老运动中探测巨魔。我们的实验表明MetTrall大大超出基准和最新简陋的文本分类模型。最后,我们探索了将MetalTroll扩大到多语种和多式检测的简单方法。Metaltroll的源代码代码可以在: http://giastrollol.</s>