🤖 AI Summary
This work proposes the first DISARM-oriented multi-agent AI system to address the challenges of automated detection and standardized categorization in large-scale application of the DISARM framework against foreign information manipulation and interference (FIMI) on social media. The system integrates natural language processing with knowledge mapping techniques, enabling collaborative agents to automatically identify manipulative content and transparently map it to the DISARM standard taxonomy in an interpretable manner. Experimental evaluation on two real-world annotated datasets demonstrates that the proposed approach significantly enhances FIMI analysis efficiency, strengthens situational awareness, and facilitates cross-organizational data interoperability.
📝 Abstract
The interoperability of data and intelligence across allied partners and their respective end-user groups is considered a foundational enabler to the collective defense capability--both conventional and hybrid--of NATO countries. Foreign Information Manipulation and Interference (FIMI) and related hybrid activities are conducted across various societal dimensions and infospheres, posing an ever greater challenge to the characterization of threats, sustaining situational awareness, and response coordination. Recent advances in AI have further led to the decreasing cost of AI-augmented trolling and interference activities, such as through the generation and amplification of manipulative content. Despite the introduction of the DISARM framework as a standardized metadata and analytical framework for FIMI, operationalizing it at the scale of social media remains a challenge. We propose a framework-agnostic agent-based operationalization of DISARM to investigate FIMI on social media. We develop a multi-agent pipeline in which specialized agentic AI components collaboratively (1) detect candidate manipulative behaviors, and (2) map these behaviors onto standard DISARM taxonomies in a transparent manner. We evaluated the approach on two real-world datasets annotated by domain practitioners. We demonstrate that our approach is effective in scaling the predominantly manual and heavily interpretive work of FIMI analysis, providing a direct contribution to enhancing the situational awareness and data interoperability in the context of operating in media and information-rich settings.