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:
this https URL.

MetaTroll 是一种基于元学习框架的文本模型，利用少量标注样本进行 few-shot 转移，通过引入 “特定于活动” 的转换器适配器以针对新的活动目标，可以防止模型因为不断适应而忘记如何检测老活动中的恶意用户。实验表明，MetaTroll 明显优于基线和最先进的几种少样本文本分类模型，并且可以扩展到多语言和多模态检测。