Social media platforms have become vital spaces for public discourse, serving as modern agor\`as where a wide range of voices influence societal narratives. However, their open nature also makes them vulnerable to exploitation by malicious actors, including state-sponsored entities, who can conduct information operations (IOs) to manipulate public opinion. The spread of misinformation, false news, and misleading claims threatens democratic processes and societal cohesion, making it crucial to develop methods for the timely detection of inauthentic activity to protect the integrity of online discourse. In this work, we introduce a methodology designed to identify users orchestrating information operations, a.k.a. IO drivers, across various influence campaigns. Our framework, named IOHunter, leverages the combined strengths of Language Models and Graph Neural Networks to improve generalization in supervised, scarcely-supervised, and cross-IO contexts. Our approach achieves state-of-the-art performance across multiple sets of IOs originating from six countries, significantly surpassing existing approaches. This research marks a step toward developing Graph Foundation Models specifically tailored for the task of IO detection on social media platforms.

翻译：社交媒体平台已成为公共话语的重要空间，作为现代公共领域，汇集了影响社会叙事的多元声音。然而，其开放性也使其易受恶意行为者（包括国家支持实体）的利用，这些行为者可通过信息操作来操纵公众舆论。错误信息、虚假新闻和误导性言论的传播威胁民主进程与社会凝聚力，因此开发及时检测非真实活动的方法对维护在线话语的完整性至关重要。本研究提出一种旨在识别跨多种影响力活动中策划信息操作的用户（即IO驱动者）的方法。我们提出的框架IOHunter结合了语言模型与图神经网络的优势，以提升在监督、弱监督及跨IO场景下的泛化能力。该方法在源自六个国家的多组信息操作数据集上取得了最先进的性能，显著超越了现有方法。本研究标志着为社交媒体平台信息操作检测任务定制图基础模型的重要进展。