  Coordinated online behaviors are an essential part of information and
influence operations, as they allow a more effective disinformation's spread.
Most studies on coordinated behaviors involved manual investigations, and the
few existing computational approaches make bold assumptions or oversimplify the
problem to make it tractable. Here, we propose a new network-based framework
for uncovering and studying coordinated behaviors on social media. Our research
extends existing systems and goes beyond limiting binary classifications of
coordinated and uncoordinated behaviors. It allows to expose different
coordination patterns and to estimate the degree of coordination that
characterizes diverse communities. We apply our framework to a dataset
collected during the 2019 UK General Election, detecting and characterizing
coordinated communities that participated in the electoral debate. Our work
conveys both theoretical and practical implications and provides more nuanced
and fine-grained results for studying online information manipulation.
