# Predicting the Role of Political Trolls in Social Media

arXiv: https://arxiv.org/abs/1910.02001 · HTML: https://mlsanity.com/paper/1910.02001/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls](https://mlsanity.com/paper/2208.00176/) (2022)
- [MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters](https://mlsanity.com/paper/2303.07354/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [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)
- [TrollsWithOpinion: A Dataset for Predicting Domain-specific Opinion Manipulation in Troll Memes](https://mlsanity.com/paper/2109.03571/) (2021)
- [Mapping the Russian Internet Troll Network on Twitter using a Predictive Model](https://mlsanity.com/paper/2409.08305/) (2024)
- [Beyond Content: Behavioral Policies Reveal Actors in Information Operations](https://mlsanity.com/paper/2602.02838/) (2026)

## Abstract (arXiv)

We investigate the political roles of "Internet trolls" in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and labor-intensive, thus making it impractical as a first-response tool for newly-discovered troll farms. In this paper, we show how to automate this analysis by using machine learning in a realistic setting. In particular, we show how to classify trolls according to their political role ---left, news feed, right--- by using features extracted from social media, i.e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls. Technically, we leverage the community structure and the text of the messages in the online social network of trolls represented as a graph, from which we extract several types of learned representations, i.e.,~embeddings, for the trolls. Experiments on the "IRA Russian Troll" dataset show that our methodology improves over the state-of-the-art in the first scenario, while providing a compelling case for the second scenario, which has not been explored in the literature thus far.