Abstract

Online influence operations (IOs) pose a critical threat to the integrity of public discourse on social media, yet their early detection and accurate prediction of user engagement remain open challenges. This work presents a comprehensive computational framework for understanding influence operations, addressing two interconnected questions: How can IO drivers be distinguished from legitimate users? And can future interactions between IO drivers and organic users be predicted? We analyze temporal interaction networks from five real-world influence campaigns attributed to state-sponsored actors from China, Cuba, Iran, Russia, and the United Arab Emirates. To motivate the importance of detecting IOs and predicting engagement with their content, we first provide empirical evidence that, in four of the five campaigns we study, organic user adoption of IO-pushed content (e.g., hashtags and URLs) is statistically …

Date

2026

Authors

Simone Mungari, Emilio Ferrara, Luca Luceri

Journal

EPJ Data Science

Publisher

Springer Berlin Heidelberg