# Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Web

arXiv: https://arxiv.org/abs/1801.09288 · HTML: https://mlsanity.com/paper/1801.09288/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2018.

## Built on by (most cited first, 18 of 18)

- [A Simulation System Towards Solving Societal-Scale Manipulation](https://mlsanity.com/paper/2410.13915/) (2024)
- [Coordinating Narratives and the Capitol Riots on Parler](https://mlsanity.com/paper/2109.00945/) (2021)
- [MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters](https://mlsanity.com/paper/2303.07354/) (2023)
- [Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale](https://mlsanity.com/paper/2308.02068/) (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)
- [Multimodal and Explainable Internet Meme Classification](https://mlsanity.com/paper/2212.05612/) (2022)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [The Rise and Fall of Fake News sites: A Traffic Analysis](https://mlsanity.com/paper/2103.09258/) (2021)
- [SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter](https://mlsanity.com/paper/2312.11553/) (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)
- [NLP Case Study on Predicting the Before and After of the Ukraine-Russia and Hamas-Israel Conflicts](https://mlsanity.com/paper/2410.06427/) (2024)
- [Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends](https://mlsanity.com/paper/2503.00599/) (2025)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)
- [Density-aware Walks for Coordinated Campaign Detection](https://mlsanity.com/paper/2506.13912/) (2025)
- [Beyond Content: Behavioral Policies Reveal Actors in Information Operations](https://mlsanity.com/paper/2602.02838/) (2026)
- [Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations](https://mlsanity.com/paper/2607.14491/) (2026)
- [Not All Duplicates Are Coordination: Generic vs. Non-Generic Duplicate Campaigns in Information Operations](https://mlsanity.com/paper/2609.13671/) (2026)

## Abstract (arXiv)

Over the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or "trolls." Although they are often involved in spreading disinformation on social media, there is little understanding of how these trolls operate, what type of content they disseminate, and most importantly their influence on the information ecosystem. In this paper, we shed light on these questions by analyzing 27K tweets posted by 1K Twitter users identified as having ties with Russia's Internet Research Agency and thus likely state-sponsored trolls. We compare their behavior to a random set of Twitter users, finding interesting differences in terms of the content they disseminate, the evolution of their account, as well as their general behavior and use of Twitter. Then, using Hawkes Processes, we quantify the influence that trolls had on the dissemination of news on social platforms like Twitter, Reddit, and 4chan. Overall, our findings indicate that Russian trolls managed to stay active for long periods of time and to reach a substantial number of Twitter users with their tweets. When looking at their ability of spreading news content and making it viral, however, we find that their effect on social platforms was minor, with the significant exception of news published by the Russian state-sponsored news outlet RT (Russia Today).