Built on peer-reviewed research.
Newjee's methods are published. Three of our papers were accepted in 2026, at NLPAICS and at two LREC workshops, and are on the ACL Anthology and arXiv.
NLPAICS 2026
Bloc-Conditional Event States: Measuring Cross-Coverage Divergence for Threat-Intelligence Analysis
Maryam Fooladi, Federico Bottino
Content-level measurement of cross-bloc framing divergence in news coverage of contested events. Each editorial bloc is represented as a density matrix ρ on a 15-dim framing-feature space (5 Semetko–Valkenburg frames · 4 dimension features · 6 Da San Martino manipulation indicators); trace distance D(ρ_state, ρ_mainstream) measures cross-bloc divergence. Three inferential layers — bloc-permutation null, within-bloc bootstrap, leave-one-outlet-out — test partition-construction circularity, sample stability, and outlet-level robustness against an enumerated within-mainstream baseline. The leading rank-2 subspace decomposes into a frame-axis and a manipulation-axis pair on both case studies (Hormuz blockade 2026, n = 16; Navalny death 2024, n = 14), positioning the bloc-difference operator as a content-level analogue to the source-level signals threat-intelligence workflows currently rely on.
PoliticalNLP 2026 · LREC 2026
Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation
Maryam Fooladi, Federico Bottino
Comparative study of RoBERTa-based sentiment classifiers vs. LLM-based multi-dimensional analysis on political news. Identifies the "neutral collapse" — RoBERTa labels ~70% of political articles as neutral, with 23% of those carrying negative-probability scores above 0.30 — and demonstrates that LLM-based frameworks expose framing direction and intensity, sensationalism, emotional appeal, and ideological lean that single-axis sentiment hides. Applied to a corpus of 50 political articles from 17 international outlets.
InDor Workshop · LREC 2026
A Multi-Layer AI Framework for Information Landscape Analysis
Maryam Fooladi, Federico Bottino
Multi-layer extraction architecture that turns raw news signals into the 15-dim epistemic vector at the heart of Newjee. Establishes the Frame · Dimension · Manipulation block decomposition and the per-feature evidence-span requirement that grounds every score in verbatim text. The architecture separates strategic narrative interpretation, operational extraction, regulatory mapping, and human-AI collaboration into distinct layers with structured interfaces — replacing the monolithic-LLM approach that fails on information-landscape analysis.
