Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls
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Abstract (arXiv)
Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused "trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's information ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence automated detection is not a straightforward task. Using the Hawkes Processes statistical model, we quantify the influence these accounts have on pushing URLs on four social platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab. In general, Russian trolls were more influential and efficient in pushing URLs to all the other platforms with the exception of /pol/ where Iranians were more influential. Finally, we release our data and source code to ensure the reproducibility of our results and to encourage other researchers to work on understanding other emerging kinds of state-sponsored troll accounts on Twitter.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Nov 2018.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
9 papers.
- 2021VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Mediacited by 14
- 2022Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trollscited by 8
- 2022Multimodal and Explainable Internet Meme Classificationcited by 5
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulationscited by 3
- 2021Divided We Rule: Influencer Polarization on Twitter During Political Crises in Indiacited by 2
- 2023Inductive detection of Influence Operations via Graph Learningcited by 2
- 2022Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimizationcited by 1
- 2022A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweetscited by 0
- 2026Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operationscited by 0
- 2026Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations16 Jul 2026
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations24 Aug 2023
- 2023Inductive detection of Influence Operations via Graph Learning26 May 2023
- 2022Multimodal and Explainable Internet Meme Classification11 Dec 2022
- 2022Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls17 Oct 2022
- 2022A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets17 Jul 2022
- 2022Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization23 Mar 2022
- 2021VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media28 Oct 2021
- 2021Divided We Rule: Influencer Polarization on Twitter During Political Crises in India18 May 2021