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. \textit{IO drivers}, across various influence campaigns. Our framework, named \texttt{IOHunter}, leverages the combined strengths of Language Models and Graph Neural Networks to improve generalization in \emph{supervised}, \emph{scarcely-supervised}, and \emph{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讨论的重要空间，作为现代广场，各种声音在此影响社会叙事。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，这些行为者可通过开展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和社会凝聚力，因此开发及时检测非真实活动的方法以保护在线讨论的完整性至关重要。在本研究中，我们提出了一种旨在识别跨不同影响力活动中策划信息操作的用户（即\textit{IO驱动者}）的方法。我们的框架名为\texttt{IOHunter}，结合了语言模型和图神经网络的各自优势，以提升在\emph{有监督}、\emph{弱监督}和\emph{跨IO}场景下的泛化能力。我们的方法在源自六个国家的多组信息操作数据集上实现了最先进的性能，显著超越了现有方法。这项研究标志着向开发专门针对社交媒体平台信息操作检测任务的图基础模型迈出了一步。