Abstract

Detecting organized political campaigns, commonly known as astroturf campaigns, is of paramount importance in fighting against disinformation on social media. Existing approaches for the identification of such organized actions employ techniques mostly from network science, graph machine learning and natural language processing. Their ultimate goal is to analyze the relationships and interactions (e.g. re-posting) among users and the textual similarities of their posts. Despite their effectiveness in recognizing astroturf campaigns, these methods face significant challenges, notably the class imbalance in available training datasets. To mitigate this issue, recent methods usually resort to data augmentation or increasing the number of positive samples, which may not always be feasible or sufficient in real-world settings. Following a different path, in this paper, we propose a novel framework for identifying astroturf …

Date

2025

Authors

Nikos Kanakaris, Heng Ping, Xiongye Xiao, Nesreen K Ahmed, Luca Luceri, Emilio Ferrara, Paul Bogdan

Book

Companion Proceedings of the ACM on Web Conference 2025

Pages

2651-2660