Blaming Across the Aisle: Political Contrasting and Blame Attribution in the Danish Parliament
cs.CL
Submitted: 2026-09-22
Updated: 2026-09-22
Comments: 8 Pages + appendix (25 total) Main paper 4 figures 2 tables: Appendix 9 figures 10 tables. Model found here: https://huggingface.co/Lundsfryd/BlameBERT , dataset here: https://huggingface.co/datasets/runetrust/blame-folketinget-dk. Markus Lundsfryd Jensen and Rune Egeskov Trust have contributed equally. Paper will be submitted through ACL rolling review (ARR), we are aiming for COLING 2027
License: http://creativecommons.org/licenses/by/4.0/
The gist: Political discourse is widely perceived to be growing more hostile, yet robust evidence remains scarce.
Terminology
Abstract
Political discourse is widely perceived to be growing more hostile, yet robust evidence remains scarce. This study examines blame attribution in the Danish Parliament from 1997 to 2026, combining a purpose-built classifier, BlameBERT (F1: 0.80), with multilevel statistical modeling. The classifier is constructed using an annotation-efficient pipeline for blame attribution in low-to-mid resource languages. The results reveal a banana-shaped trajectory, with blame declining until around 2016 before entering a significant and sustained increase in recent years (2019-2026). Government status consistently influenced blame attribution - an effect we term political contrasting - with opposition parties blaming substantially more than governing parties. This effect was moderated by ideology: The blame-dampening effect of governing was less pronounced among right-wing parties, and ideological extremity amplified blame more strongly on the right. In recent years, the interaction between political wing and ideological extremity intensified, suggesting an ideological hardening of the blame rhetoric concentrated on the right of the political spectrum. Taken together, these patterns suggest that the perceived rise in harsh political language reflects not merely a general rhetorical drift, but an ideologically asymmetric hardening of political discourse. A sensitivity analysis showed that the conclusions were robust to varying classification thresholds.
Sources
- Addressing the Challenges of Cross-Lingual Hate Speech Detection
- Blameocracy: Causal Rhetoric in Politics
- LoRA: Low-Rank Adaptation of Large Language Models
- DaCy: A Unified Framework for Danish NLP
- mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
- Hopes and Fears -- Emotion Distribution in the Topic Landscape of Finnish Parliamentary Speech 2000-2020
- Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification
- Democratizing Neural Machine Translation with OPUS-MT
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering