The detection of state-sponsored trolls acting in information operations is an unsolved and critical challenge for the research community, with repercussions that go beyond the online realm. In this paper, we propose a novel AI-based solution for the detection of state-sponsored troll accounts, which consists of two steps. The first step aims at classifying trajectories of accounts' online activities as belonging to either a state-sponsored troll or to an organic user account. In the second step, we exploit the classified trajectories to compute a metric, namely "troll score", which allows us to quantify the extent to which an account behaves like a state-sponsored troll. As a study case, we consider the troll accounts involved in the Russian interference campaign during the 2016 US Presidential election, identified as Russian trolls by the US Congress. Experimental results show that our approach identifies accounts' trajectories with an AUC close to 99% and, accordingly, classify Russian trolls and organic users with an AUC of 90%. Finally, we evaluate whether the proposed solution can be generalized to different contexts (e.g., discussions about Covid-19) and generic misbehaving users, showing promising results that will be further expanded in our future endeavors.

翻译：检测信息行动中由国家支持的水军活动，是研究社区面临的一项未解决且关键性挑战，其影响超出了网络空间。本文提出了一种基于人工智能的新型解决方案，用于检测国家支持的水军账号，该方案包含两个步骤。第一步旨在将账号在线活动轨迹分类为属于国家支持的水军或有机用户账号。第二步则利用分类后的轨迹计算一个指标，即“水军分数”，用于量化账号行为与国家支持水军的相似程度。以2016年美国总统大选期间俄罗斯干预行动中的水军账号为研究案例，这些账号被美国国会认定为俄罗斯水军。实验结果显示，我们的方法识别账号轨迹的AUC接近99%，并将俄罗斯水军和有机用户分类的AUC达到90%。最后，我们评估了该解决方案在不同场景（如关于新冠疫情的讨论）和通用不当行为用户中的泛化能力，结果令人期待，将在未来工作中进一步拓展。