Unsupervised Social Bot Detection via Structural Information Theory: An Effective and Interpretable Framework

This work proposes an effective, practical, and interpretable unsupervised framework, UnDBot, for detecting social bots based on structural information theory. UnDBot constructs a multi-relational graph to model the similarity of user behaviors, optimizes the heterogeneous structural entropy to achieve hierarchical community partitioning, and identifies social bot communities by integrating community influence and cohesion.