@inproceedings{fooladi-bottino-2026-bloc,
    title = "Bloc-Conditional Event States: Measuring Cross-Coverage Divergence for Threat-Intelligence Analysis",
    author = "Fooladi, Maryam  and
      Bottino, Federico",
    editor = "Mitkov, Ruslan  and
      Mu{\~n}oz, Rafael  and
      Lloret, Elena  and
      Ranasinghe, Tharindu  and
      Estevanell-Valladares, Ernesto L.  and
      Lamsiyah, Salima  and
      Montoyo, Andr{\'e}s  and
      Ezzini, Saad",
    booktitle = "Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    month = jun,
    year = "2026",
    address = "Alicante, Spain",
    publisher = "Department of Languages and Information Systems, University of Alicante",
    url = "https://aclanthology.org/2026.nlpaics-1.10/",
    pages = "98--102",
    abstract = "We propose a content-level measurement of cross-bloc framing divergence in news coverage of contested events, built on the eventstate ({\ensuremath{\rho}}e) formalism of Bottino et al. (2026). For a given event, outlets are aggregated into editorially-coherent blocs and each bloc is represented by a density matrix {\ensuremath{\rho}} bloc on a 15-dimensional framing space. The trace distance D({\ensuremath{\rho}} state, {\ensuremath{\rho}}mainstream) measures cross-bloc divergence; benchmarking it against the withinWestern polarization D({\ensuremath{\rho}} right, {\ensuremath{\rho}}left) controls for editorial variation. The top eigenvector of ({\ensuremath{\rho}} state {\ensuremath{-}} {\ensuremath{\rho}} mainstream) attributes divergence to specific framing axes. Two case studies (Hormuz blockade 2026, n = 16; Navalny death 2024, n = 14) demonstrate the construction. The work positions D({\ensuremath{\rho}} state, {\ensuremath{\rho}}mainstream) as a content-level observable of potential interest to threat-intelligence workflows that currently rely on source-level features."
}
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%0 Conference Proceedings
%T Bloc-Conditional Event States: Measuring Cross-Coverage Divergence for Threat-Intelligence Analysis
%A Fooladi, Maryam
%A Bottino, Federico
%Y Mitkov, Ruslan
%Y Muñoz, Rafael
%Y Lloret, Elena
%Y Ranasinghe, Tharindu
%Y Estevanell-Valladares, Ernesto L.
%Y Lamsiyah, Salima
%Y Montoyo, Andrés
%Y Ezzini, Saad
%S Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2026
%8 June
%I Department of Languages and Information Systems, University of Alicante
%C Alicante, Spain
%F fooladi-bottino-2026-bloc
%X We propose a content-level measurement of cross-bloc framing divergence in news coverage of contested events, built on the eventstate (\ensuremathρe) formalism of Bottino et al. (2026). For a given event, outlets are aggregated into editorially-coherent blocs and each bloc is represented by a density matrix \ensuremathρ bloc on a 15-dimensional framing space. The trace distance D(\ensuremathρ state, \ensuremathρmainstream) measures cross-bloc divergence; benchmarking it against the withinWestern polarization D(\ensuremathρ right, \ensuremathρleft) controls for editorial variation. The top eigenvector of (\ensuremathρ state \ensuremath- \ensuremathρ mainstream) attributes divergence to specific framing axes. Two case studies (Hormuz blockade 2026, n = 16; Navalny death 2024, n = 14) demonstrate the construction. The work positions D(\ensuremathρ state, \ensuremathρmainstream) as a content-level observable of potential interest to threat-intelligence workflows that currently rely on source-level features.
%U https://aclanthology.org/2026.nlpaics-1.10/
%P 98-102
Markdown (Informal)

[Bloc-Conditional Event States: Measuring Cross-Coverage Divergence for Threat-Intelligence Analysis](https://aclanthology.org/2026.nlpaics-1.10/) (Fooladi & Bottino, NLPAICS 2026)

ACL