  The argument for persistent social media influence campaigns, often funded by
malicious entities, is gaining traction. These entities utilize instrumented
profiles to disseminate divisive content and disinformation, shaping public
perception. Despite ample evidence of these instrumented profiles, few
identification methods exist to locate them in the wild. To evade detection and
appear genuine, small clusters of instrumented profiles engage in unrelated
discussions, diverting attention from their true goals. This strategic thematic
diversity conceals their selective polarity towards certain topics and fosters
public trust.
  This study aims to characterize profiles potentially used for influence
operations, termed 'on-mission profiles,' relying solely on thematic content
diversity within unlabeled data. Distinguishing this work is its focus on
content volume and toxicity towards specific themes. Longitudinal data from
138K Twitter or X, profiles and 293M tweets enables profiling based on theme
diversity. High thematic diversity groups predominantly produce toxic content
concerning specific themes, like politics, health, and news classifying them as
'on-mission' profiles.
  Using the identified ``on-mission" profiles, we design a classifier for
unseen, unlabeled data. Employing a linear SVM model, we train and test it on
an 80/20% split of the most diverse profiles. The classifier achieves a
flawless 100% accuracy, facilitating the discovery of previously unknown
``on-mission" profiles in the wild.
