![[2601.15109] An Agentic Operationalization of DISARM for FIMI Investigation on Social Media](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2601.15109] An Agentic Operationalization of DISARM for FIMI Investigation on Social Media
Summary
This article presents a framework-agnostic, agent-based operationalization of the DISARM framework to investigate Foreign Information Manipulation and Interference (FIMI) on social media, enhancing situational awareness and data interoperability.
Why It Matters
As social media becomes a battleground for information manipulation, understanding and operationalizing frameworks like DISARM is crucial for NATO and allied countries. This research addresses the challenges of detecting and categorizing manipulative behaviors, which is essential for maintaining security and public trust in information sources.
Key Takeaways
- The DISARM framework is essential for analyzing FIMI on social media.
- Agent-based AI can effectively detect manipulative behaviors in real-time.
- The proposed method enhances situational awareness and data interoperability.
- Real-world application revealed previously undetected manipulative accounts.
- Collaboration among AI components is vital for effective analysis.
Computer Science > Social and Information Networks arXiv:2601.15109 (cs) [Submitted on 21 Jan 2026 (v1), last revised 16 Feb 2026 (this version, v2)] Title:An Agentic Operationalization of DISARM for FIMI Investigation on Social Media Authors:Kevin Tseng, Juan Carlos Toledano, Bart De Clerck, Yuliia Dukach, Phil Tinn View a PDF of the paper titled An Agentic Operationalization of DISARM for FIMI Investigation on Social Media, by Kevin Tseng and 4 other authors View PDF HTML (experimental) Abstract:The interoperability of data and intelligence across allied partners and their respective end-user groups is considered a foundational enabler of 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 threat characterization, sustained situational awareness, and response coordination. Recent advances in AI have further reduced the 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 an ...
