Abstract
Our ability to study the fine‐scale behavior of cetaceans is evolving rapidly with the use of drones (or unoccupied aircraft systems) as platforms to observe and record behavior and with the emergence of machine learning and data science tools to quantify these observations. Together, these tools can provide an unprecedented perspective on the surface and near‐surface behaviors of cetaceans and the dynamics of their social groups. However, the use of machine learning to study cetacean behavior lags behind its application in other areas, such as cetacean acoustics and photo‐identification. In this review, we discuss the challenges and opportunities of combining these technologies to quantify cetacean social dynamics, and we present recent advances in the development of machine learning pipelines that go beyond the detection of animals. We also consider strategies that can improve the performance of models in individual studies, and as a broader research community, to allow the development of more effective and more generalizable tools.