Evaluating the acceptance of users’ reactions to prevent the escalation of information disorder

The spread of information disorder is increasingly being addressed as a security threat that can pose a significant risk both to individual users and to entire communities. This paper presents a methodology for estimating and communicating the spread of information disorder by analyzing user reactions. The sentiment analysis of users’ comments and profiling mechanisms for community detection are exploited to measure quantitative parameters and an ontology has been defined for describing behavioral patterns of disinformation. The effectiveness of the approach is demonstrated through the validation of a software prototype that allowed us to test the proposed methodology and techniques on open and public data-sets and on original collected data. • Frames disinformation as a cyber-attack vector via ontology, integrable with STIX/TAXII threat intelligence. • Profiles user reactions using AI-driven stylometry, sentiment, and community detection on social networks. • Deploys microservices prototype for automated STIX report generation from disinformation case studies. • Clusters users (e.g., Analytical Writers, Emphatic Reactors) to quantify escalation risk in real data-sets. • Enables coordinated cybersecurity responses by modeling information disorder as structured threats.