  The prevalence of coordinated information campaigns in social media platforms
has significant negative consequences across various domains, including social,
political, and economic processes. This paper proposes a multifaceted framework
for detecting and analysing coordinated message promotion on social media. By
simultaneously considering features related to content, time, and network
dimensions, our framework can capture the diverse nature of coordinated
activity and identify anomalous user accounts who likely engaged in suspicious
behaviour. Unlike existing solutions that rely on specific constraints, our
approach is more flexible as it employs specialised components to extract the
significant structures within a network and to detect the most unusual
interactions. We demonstrate the effectiveness of our framework using two
Twitter datasets, the Russian Internet Research Agency (IRA), and long-term
discussions on Data Science topics. The results demonstrate our framework's
ability to isolate unusual activity from expected normal behaviour and provide
valuable insights for further qualitative investigation.
