Semantic Isotopies of Retribution in Pre-Attack Manifestos: An AI-Assisted Conceptual Metaphor Analysis of Moral Justification

Abstract

Pre-attack written communications encode ideological structure, yet existing computational analyses rely on prior-loaded feature spaces—hand-curated Lakoff inventories or LIWC-style counters—that confirm what the literature already expects rather than discovering what is in the data. We present a neuro-symbolic architecture for unsupervised conceptual-metaphor discovery that satisfies the auxiliary-information conditions under which the Locatello et al. non-identifiability result no longer applies. The contribution is a single, end-to-end-trainable model that operationalizes the inductive biases of four identifiability results: iVAE conditional priors, $\beta $ -Total-Correlation factorization, Slow-VAE time-contrastive identifiability, and the Graph Information Bottleneck. These are realized over a multi-relational graph—co-occurrence, syntactic-dependency, similarity, and semic-prior edges—encoded by a Multi-Head GATv2 and disentangled by an iVAE. As a methodological test bed—not an evaluation benchmark—we apply it to a perpetrator-only forensic corpus (598 sentence-windowed segments, 29 documents, 24 lone-actor authors, 1966–2024) and provide a deterministic, fully reproducible implementation of the full pipeline. Without any predefined lexicon, the model recovers a “retribution-as-debt” axis (top tokens retribution, sex, justice, degenerate) that a rule-based Lakoff lookup would miss; author-conditioning controls (perpetrator identity masked and shuffled) indicate this axis encodes content rather than author-specific vocabulary. Against matched-protocol baselines it attains the best cross-perpetrator macro-F1 (0.44 vs. 0.31 for the strongest baseline). We explicitly disclaim threat-screening applicability: with perpetrator-only data and no control group, sensitivity and specificity are not estimable; the contribution is to the mathematics of identifiable disentanglement, not to operational threat assessment.