Mapping Weaponised Victimhood: A Machine Learning Approach

Butcher, Samantha and De La Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 (2025) Mapping Weaponised Victimhood: A Machine Learning Approach. In: 17th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2025 as part of IC3K 2025 - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K - Proceedings . Science and Technology Publications, Lda, ESP, pp. 216-223. ISBN 9789897587696

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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 multitask modelling as a promising approach for decoding complex rhetorical strategies in real-world political language.

Item Type:	Book Section
Additional Information:	Publisher Copyright: Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
Uncontrolled Keywords:	bert,entity framing,multi-task learning,named entity recognition,natural language processing,political discourse,software,strategy and management,management of technology and innovation ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging Faculty of Science > Research Groups > Health Computing Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	10 Aug 2026 13:45
Last Modified:	10 Aug 2026 13:45
URI:	https://ueaeprints.uea.ac.uk/id/eprint/104084
DOI:	10.5220/0013673600004000

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