# X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents

arXiv: https://arxiv.org/abs/2508.16021 · HTML: https://mlsanity.com/paper/2508.16021/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 59 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Rationalizing Neural Predictions](https://mlsanity.com/paper/1606.04155/) (2016)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Interpretable Rumor Detection in Microblogs by Attending to User Interactions](https://mlsanity.com/paper/2001.10667/) (2020)
- [Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web](https://mlsanity.com/paper/1801.09288/) (2018)
- [FR: Folded Rationalization with a Unified Encoder](https://mlsanity.com/paper/2209.08285/) (2022)
- [Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems](https://mlsanity.com/paper/2211.14114/) (2022)
- [Transfer Learning for Hate Speech Detection in Social Media](https://mlsanity.com/paper/1906.03829/) (2019)
- [MGR: Multi-generator Based Rationalization](https://mlsanity.com/paper/2305.04492/) (2023)
- [Still out there: Modeling and Identifying Russian Troll Accounts on Twitter](https://mlsanity.com/paper/1901.11162/) (2019)
- [Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions](https://mlsanity.com/paper/2109.00302/) (2021)
- [Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures](https://mlsanity.com/paper/2208.10598/) (2022)
- [#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate](https://mlsanity.com/paper/1802.09808/) (2018)
- [Who Falls for Online Political Manipulation?](https://mlsanity.com/paper/1808.03281/) (2018)
- [MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters](https://mlsanity.com/paper/2303.07354/) (2023)
- [Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study](https://mlsanity.com/paper/2502.02943/) (2025)
- [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)
- [Practical Guidelines for Ideology Detection Pipelines and Psychosocial Applications](https://mlsanity.com/paper/2208.04097/) (2022)
- [Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions](https://mlsanity.com/paper/2208.06620/) (2022)
- ['Senator, We Sell Ads': Analysis of the 2016 Russian Facebook Ads Campaign](https://mlsanity.com/paper/1809.10158/) (2018)
- [MVAN: Multi-View Attention Networks for Fake News Detection on Social Media](https://mlsanity.com/paper/2506.01627/) (2025)
- [Analysing user identity via time-sensitive semantic edit distance (t-SED): A case study of Russian trolls on Twitter](https://mlsanity.com/paper/1901.05228/) (2019)
- [Birdspotter: A Tool for Analyzing and Labeling Twitter Users](https://mlsanity.com/paper/2012.02370/) (2020)

## Built on by (most cited first, 2 of 2)

- [DREAMS: A Social Exchange Theory-Informed Modeling of Misinformation Engagement on Social Media](https://mlsanity.com/paper/2602.01567/) (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, malicious actors who deploy sophisticated linguistic manipulation in coordinated information campaigns, posing threats to online discourse integrity. While Large Language Models (LLMs) achieve strong performance on general natural language processing (NLP) tasks, they struggle with subtle propaganda detection and operate as ``black boxes'', providing no interpretable insights into manipulation strategies. This paper introduces X-Troll, a novel framework that bridges this gap by integrating explainable adapter-based LLMs with expert-derived linguistic knowledge to detect state-sponsored trolls and provide human-readable explanations for its decisions. X-Troll incorporates appraisal theory and propaganda analysis through specialized LoRA adapters, using dynamic gating to capture campaign-specific discourse patterns in coordinated information operations. Experiments on real-world data demonstrate that our linguistically-informed approach shows strong performance compared with both general LLM baselines and existing troll detection models in accuracy while providing enhanced transparency through expert-grounded explanations that reveal the specific linguistic strategies used by state-sponsored actors. X-Troll source code is available at: https://github.com/ltian678/xtroll_source/.