This paper presents a novel framework for the thorough analysis of fake news and disinformation campaigns, which have the potential to result in both offline and online criminal activities. Its primary focus relies on the spread analysis of disinformation across social media and online platforms, aiming to uncover the underlying dynamics and mechanisms driving the dissemination of false information. The framework integrates state-of-the-art Natural Language Processing (NLP) techniques for sentiment analysis, Deep Learning (DL) algorithms for prediction of criminal activties related to the disiformation spread and graph analysis to identify key actors and propagation pathways. To address the emerging challenges of disinformation that transcend the online realm and have tangible real-world consequences, the framework extends its analysis to potential offline actions incited by disinformation, such as acts of violence and public unrest or the disruption of public health efforts especially in case of pandemics. By exploring the complex interconnections between disinformation and crimes, our research aims to contribute to a deeper understanding of the societal implications of false information and provide actionable insights for policymakers, security practitioners and the broader public.