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 strategy evident in many social engineering plots. Be it news or messages, the 21st century forces is overfilled with content, forcing people into constant stress, weakening their decision-making, and increasing their susceptibility to manipulation. We discuss the practical overview of disinformation detection. In this discussion, we include uncertainty quantification (UQ) as a groundbreaking tool to counteract this challenge (a solution introduced by Puczynska et al. [3]). UQ enhances reliability, explainability, and adaptability in disinformation detection systems, as it enables estimation of model confidence. Our framework demonstrates the potential of AI-driven systems to counteract disinformation through multimodal analysis and cross-platform collaboration while maintaining transparency and ethical integrity. We underscore the urgency of integrating UQ into fake news detection methodologies to address the rapid evolution of disinformation campaigns. The paper concludes by outlining future directions for developing scalable, transparent, and resilient systems to safeguard information integrity and societal trust in an increasingly digital age.