Inductive detection of Influence Operations via Graph Learning
Nicholas A. Gabriel David A. Broniatowski Neil F. Johnson
Influence operations are large-scale efforts to manipulate public opinion.
The rapid detection and disruption of these operations is critical for healthy
public discourse. Emergent AI technologies may enable novel operations which
evade current detection methods and influence public discourse on social media
with greater scale, reach, and specificity. New methods with inductive learning
capacity will be needed to identify these novel operations before they
indelibly alter public opinion and events. We develop an inductive learning
framework which: 1) determines content- and graph-based indicators that are not
specific to any operation; 2) uses graph learning to encode abstract signatures
of coordinated manipulation; and 3) evaluates generalization capacity by
training and testing models across operations originating from Russia, China,
and Iran. We find that this framework enables strong cross-operation
generalization while also revealing salient
indicators$\unicode{x2013}$illustrating a generic approach which directly
complements transductive methodologies, thereby enhancing detection coverage.