# Labeled Datasets for Research on Information Operations

arXiv: https://arxiv.org/abs/2411.10609 · HTML: https://mlsanity.com/paper/2411.10609/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [An Agentic Operationalization of DISARM for FIMI Investigation on Social Media](https://mlsanity.com/paper/2601.15109/) (2026)
- [Multimodal Coordinated Online Behavior: Trade-offs and Strategies](https://mlsanity.com/paper/2507.12108/) (2025)
- [Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns](https://mlsanity.com/paper/2607.05855/) (2026)
- [Manufactured Divisiveness: Decomposing the Hostile Content of Seven Social Media Influence Operations](https://mlsanity.com/paper/2607.14491/) (2026)

## Abstract (arXiv)

Social media platforms have become a hub for political activities and discussions, democratizing participation in these endeavors. However, they have also become an incubator for manipulation campaigns, like information operations (IOs). Some social media platforms have released datasets related to such IOs originating from different countries. However, we lack comprehensive control data that can enable the development of IO detection methods. To bridge this gap, we present new labeled datasets about 26 campaigns, which contain both IO posts verified by a social media platform and over 13M posts by 303k accounts that discussed similar topics in the same time frames (control data). The datasets will facilitate the study of narratives, network interactions, and engagement strategies employed by coordinated accounts across various campaigns and countries. By comparing these coordinated accounts against organic ones, researchers can develop and benchmark IO detection algorithms.