# MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters

arXiv: https://arxiv.org/abs/2303.07354 · HTML: https://mlsanity.com/paper/2303.07354/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [Conditional Neural Processes](https://mlsanity.com/paper/1807.01613/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes](https://mlsanity.com/paper/1906.07697/) (2019)
- [Induction Networks for Few-Shot Text Classification](https://mlsanity.com/paper/1902.10482/) (2019)
- [Diverse Few-Shot Text Classification with Multiple Metrics](https://mlsanity.com/paper/1805.07513/) (2018)
- [Few-shot Text Classification with Distributional Signatures](https://mlsanity.com/paper/1908.06039/) (2019)
- [Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension](https://mlsanity.com/paper/1911.00202/) (2019)
- [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018)
- [Antisocial Behavior in Online Discussion Communities](https://mlsanity.com/paper/1504.00680/) (2015)
- [Addressing Catastrophic Forgetting in Few-Shot Problems](https://mlsanity.com/paper/2005.00146/) (2020)
- [Still out there: Modeling and Identifying Russian Troll Accounts on Twitter](https://mlsanity.com/paper/1901.11162/) (2019)
- [Predicting the Role of Political Trolls in Social Media](https://mlsanity.com/paper/1910.02001/) (2019)
- [Who Falls for Online Political Manipulation?](https://mlsanity.com/paper/1808.03281/) (2018)
- ['Senator, We Sell Ads': Analysis of the 2016 Russian Facebook Ads Campaign](https://mlsanity.com/paper/1809.10158/) (2018)
- [Exposing Paid Opinion Manipulation Trolls](https://mlsanity.com/paper/2109.13726/) (2021)
- [Trolls Identification within an Uncertain Framework](https://mlsanity.com/paper/1501.05272/) (2015)

## Built on by (most cited first, 7 of 7)

- [Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models](https://mlsanity.com/paper/2310.05253/) (2023)
- [Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement](https://mlsanity.com/paper/2502.04655/) (2025)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025)
- [Detecting Sockpuppetry on Wikipedia Using Meta-Learning](https://mlsanity.com/paper/2506.10314/) (2025)
- [Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches](https://mlsanity.com/paper/2605.23910/) (2026)
- [Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?](https://mlsanity.com/paper/2609.35809/) (2026)

## Abstract (arXiv)

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 textitcampaign-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.