We study Twitter data from a dynamical systems perspective. In particular, we focus on the large set of data released by Twitter Inc. and asserted to represent a Russian influence operation. We propose a mathematical model to describe the per-day tweet production that can be extracted using spectral analysis. We show that this mathematical model allows us to construct families (clusters) of users with common harmonics. We define a labeling scheme describing user strategy in an information operation and show that the resulting strategies correspond to the behavioral clusters identified from their harmonics. We then compare these user clusters to the ones derived from text data using a graph-based topic analysis method. We show that spectral properties of the user clusters are related to the number of user-topic groups represented in a spectral cluster. Bulk data analysis also provides new insights into the data set in the context of prior work.