  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.
