Inductive detection of Influence Operations via Graph Learning
This addresses the need for rapid detection of AI-enabled influence operations to protect public discourse, though it is an incremental improvement over existing transductive methods.
The paper tackles the problem of detecting novel influence operations on social media by developing an inductive learning framework that identifies generic content- and graph-based indicators, achieving strong cross-operation generalization across operations from Russia, China, and Iran.
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.