Before revision:  We do this by testing the proposed coordination patterns on a large-scale dataset of ten state-attributed campaigns and various benign Twitter communitieslikely to coordinate and share information amongst themselves. We show that such patterns have significant limitations. First, coordination in Twitter communities is not uncommon, especially when the online world is reacting to real-world news (e.g., US impeachment trials). Second, due to the COVID-19 pandemic, we found that political bodies increased their coordinated activity.
After revision:  Doing so would provide more evidence to human analysts as they consider takedowns. We create a time series of daily coordination networks for both Twitter disinformation campaigns and legitimate Twitter communities, and train a binary classifier based on statistical features extracted from these networks. Our results show that the classifier can predict future coordinated activity of known disinformation campaigns with high accuracy (F1 = 0.98). On the more challenging task of out-of-distribution activity classification, the COVID-19 pandemic, we found that political bodies increased their coordinated activity.