  Social media is being increasingly weaponized by state-backed actors to
elicit reactions, push narratives and sway public opinion. These are known as
Information Operations (IO). The covert nature of IO makes their detection
difficult. This is further amplified by missing data due to the user and
content removal and privacy requirements. This work advances the hypothesis
that the very reactions that Information Operations seek to elicit within the
target social systems can be used to detect them. We propose an
Interval-censored Transformer Hawkes (IC-TH) architecture and a novel data
encoding scheme to account for both observed and missing data. We derive a
novel log-likelihood function that we deploy together with a contrastive
learning procedure. We showcase the performance of IC-TH on three real-world
Twitter datasets and two learning tasks: future popularity prediction and item
category prediction. The latter is particularly significant. Using the
retweeting timing and patterns solely, we can predict the category of YouTube
videos, guess whether news publishers are reputable or controversial and, most
importantly, identify state-backed IO agent accounts. Additional qualitative
investigations uncover that the automatically discovered clusters of
Russian-backed agents appear to coordinate their behavior, activating
simultaneously to push specific narratives.
