AI in Disinformation Detection

The Russian Doppelganger campaign was a flop. It tried to target European governments and institutions with fake news and cloned websites, but its measurable impact on real users—views, likes, or shares—was minimal [1]. However, as part of ongoing efforts to influence Western media, this campaign contributes to altering online discourse and normalizing hate speech. The potential harm from such attacks has been proven to be even more extreme. Such threats require international collaboration to identify and effectively counter such campaigns. The popularization of artificial intelligence (AI) has accelerated the spread of fake news. On the other hand, AI can help us fight back even better. Leveraging AI-driven techniques—such as Natural Language Processing (NLP), multimedia analysis, and network analysis—is crucial in this fight, as well as a common language to describe hybrid attacks. Therefore, our discussion relies on the DISARM Framework, a disinformation-focused counterpart to the MITRE ATT&CK framework, designed to standardise disinformation-related terminology and analytical methods [2]. This paper is focused on a key tactic of disinformation: overwhelming the target, a s