Mapping Weaponised Victimhood: A Machine Learning Approach

Samantha Butcher, Beatriz De La Iglesia

2025

Abstract

Political discourse frequently leverages group identity and moral alignment, with weaponised victimhood (WV) standing out as a powerful rhetorical strategy. Dominant actors employ WV to frame themselves or their allies as victims, thereby justifying exclusionary or retaliatory political actions. Despite advancements in Natural Language Processing (NLP), existing computational approaches struggle to capture such subtle rhetorical framing at scale, especially when alignment is implied rather than explicitly stated. This paper introduces a dual-task framework designed to address this gap by linking Named Entity Recognition (NER) with a nuanced rhetorical positioning classification (positive, negative, or neutral - POSIT). By treating rhetorical alignment as a structured classification task tied to entity references, our approach moves beyond sentiment-based heuristics to yield a more interpretable and fine-grained analysis of political discourse. We train and compare transformer-based models (BERT, DistilBERT, RoBERTa) across Single-Task, Multi-Task, and Task-Conditioned Multi-Task Learning architectures. Our findings demonstrate that NER consistently outperformed rhetorical positioning, achieving higher F1-scores and distinct loss dynamics. While single-task learning showed wide loss disparities (e.g., BERT NER 0.45 vs POSIT 0.99), multi-task setups fostered more balanced learning, with losses converging across tasks. Multi-token rhetorical spans proved challenging but showed modest F1 gains in integrated setups. Neutral positioning remained the weakest category, though targeted improvements were observed. Models displayed greater sensitivity to polarised language (e.g., RoBERTa TC-MTL reaching 0.55 F1 on negative spans). Ultimately, entity-level F1 scores converged (NER: 0.60–0.61; POSIT: 0.50–0.52), suggesting increasingly generalisable learning and reinforcing multi-task modelling as a promising approach for decoding complex rhetorical strategies in real-world political language.

Paper Citation

in Harvard Style

Butcher S. and De La Iglesia B. (2025). Mapping Weaponised Victimhood: A Machine Learning Approach. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 216-223. DOI: 10.5220/0013673600004000

in Bibtex Style

@conference{kdir25,
author={Samantha Butcher and Beatriz De La Iglesia},
title={Mapping Weaponised Victimhood: A Machine Learning Approach},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={216-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013673600004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Mapping Weaponised Victimhood: A Machine Learning Approach
SN -
AU - Butcher S.
AU - De La Iglesia B.
PY - 2025
SP - 216
EP - 223
DO - 10.5220/0013673600004000
PB - SciTePress