Paper Detail
Antonio Scala
AI-mediated information manipulation increasingly takes the form of social cyber attacks that target trust, attention, credibility, reputation, and decision-making rather than only technical infrastructures or isolated false contents. Existing defensive approaches often oscillate between incident-level analysis, which fragments campaigns into weak signals, and attribution-first analysis, which may delay mitigation until responsibility is established. This paper proposes a SOCMINT framework based on Information Manipulation Sets (IMS) as an intermediate operational unit between individual incidents and strategic attribution. Building on the VIGINUM/EEAS use of IMS in counter-FIMI analysis, the framework treats manipulation as a coherent process involving narratives, accounts, infrastructures, temporal patterns, cross-platform migration, synthetic amplification, and cognitive targeting. The proposed pipeline moves from signal detection and diagnostic triage to IMS hypothesis construction, confidence/severity assessment, mitigation selection, and iterative update. A compact scenario illustrates how IMS-based analysis captures what content-level and attribution-first approaches miss. The paper also proposes a tabletop evaluation protocol to assess decision quality, confidence calibration, and mitigation proportionality. The main implication is that human-centred risk mitigation requires not only better detection, but also structured reasoning under uncertainty, auditable decision-making, and safeguards against over-securitising legitimate dissent.
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@article{scala2026human,
title = {Human-Centred Risk Mitigation for AI-Mediated Information Manipulation: A SOCMINT Framework Based on Information Manipulation Sets},
author = {Antonio Scala},
year = {2026},
abstract = {AI-mediated information manipulation increasingly takes the form of social cyber attacks that target trust, attention, credibility, reputation, and decision-making rather than only technical infrastructures or isolated false contents. Existing defensive approaches often oscillate between incident-level analysis, which fragments campaigns into weak signals, and attribution-first analysis, which may delay mitigation until responsibility is established. This paper proposes a SOCMINT framework based},
url = {https://arxiv.org/abs/2606.09754},
keywords = {cs.CY, cs.CR, cs.SI},
eprint = {2606.09754},
archiveprefix = {arXiv},
}
{}