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.

翻译：影响力操作是旨在操纵公众舆论的大规模活动。快速检测并阻止这些操作对于维护健康的公共话语至关重要。新兴的人工智能技术可能催生新型操作，这些操作能规避当前检测方法，并以更大规模、更广范围和更高精准度影响社交媒体上的公共话语。我们需要具备归纳学习能力的新方法，以便在这些新型操作对公共舆论和事件造成不可逆影响之前识别它们。我们开发了一个归纳学习框架，该框架能够：1）确定不特定于任何操作的内容和基于图的指标；2）利用图学习编码协调操纵的抽象特征；3）通过在源自俄罗斯、中国和伊朗的操作上训练和测试模型来评估泛化能力。我们发现，该框架能够实现跨操作的强泛化能力，同时揭示显著指标——展示了一种直接补充转导方法的通用途径，从而增强检测覆盖率。