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We study Twitter data from a dynamical systems perspective. In particular, wefocus on the large set of data released by Twitter Inc. and asserted torepresent a Russian influence operation. We propose a mathematical model todescribe the per-day tweet production that can be extracted using spectralanalysis. We show that this mathematical model allows us to construct families(clusters) of users with common harmonics. We define a labeling schemedescribing user strategy in an information operation and show that theresulting strategies correspond to the behavioral clusters identified fromtheir harmonics. We then compare these user clusters to the ones derived fromtext data using a graph-based topic analysis method. We show that spectralproperties of the user clusters are related to the number of user-topic groupsrepresented in a spectral cluster. Bulk data analysis also provides newinsights into the data set in the context of prior work.

A Dynamical Systems Perspective Reveals Coordination in Russian Twitter Operations