# Still out there: Modeling and Identifying Russian Troll Accounts on Twitter

arXiv: https://arxiv.org/abs/1901.11162 · HTML: https://mlsanity.com/paper/1901.11162/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 11 of 11)

- [Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls](https://mlsanity.com/paper/2210.08786/) (2022)
- [MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters](https://mlsanity.com/paper/2303.07354/) (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)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [Coordinated Reply Attacks in Influence Operations: Characterization and Detection](https://mlsanity.com/paper/2410.19272/) (2024)
- [Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine](https://mlsanity.com/paper/2307.12788/) (2023)
- [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025)
- [Russo-Ukrainian War: Prediction and explanation of Twitter suspension](https://mlsanity.com/paper/2306.03502/) (2023)
- [Discursive objection strategies in online comments: Developing a classification schema and validating its training](https://mlsanity.com/paper/2405.08142/) (2024)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)
- [Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns](https://mlsanity.com/paper/2607.05855/) (2026)

## Abstract (arXiv)

There is evidence that Russia's Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter - often referred to as "Russian trolls". In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K control accounts; and, 2) demonstrate that it is possible to use this model to find active accounts on Twitter still likely acting on behalf of the Russian state. Using both behavioral and linguistic features, we show that it is possible to distinguish between a troll and a non-troll with a precision of 78.5% and an AUC of 98.9%, under cross-validation. Applying the model to out-of-sample accounts still active today, we find that up to 2.6% of top journalists' mentions are occupied by Russian trolls. These findings imply that the Russian trolls are very likely still active today. Additional analysis shows that they are not merely software-controlled bots, and manage their online identities in various complex ways. Finally, we argue that if it is possible to discover these accounts using externally - accessible data, then the platforms - with access to a variety of private internal signals - should succeed at similar or better rates.