@inproceedings{rivlin-angert-mor-lan-2025-enemy,
title = "The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech",
author = "Rivlin-Angert, Naama and
Mor-Lan, Guy",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.841/",
doi = "10.18653/v1/2025.emnlp-main.841",
pages = "16634--16647",
ISBN = "979-8-89176-332-6",
abstract = "We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from parliamentary speeches (1993-2023), Facebook posts, and leading news outlets (2018-2021), of which 1,812 instances (17.4{\%}) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline, and benchmark finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F$_1$ of 0.74 for binary PDD detection and a macro-F$_1$ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male politicians than by their female counterparts, and stronger tendencies among right-leaning actors, with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for analyzing democratic discourse."
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<abstract>We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from parliamentary speeches (1993-2023), Facebook posts, and leading news outlets (2018-2021), of which 1,812 instances (17.4%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline, and benchmark finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F₁ of 0.74 for binary PDD detection and a macro-F₁ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male politicians than by their female counterparts, and stronger tendencies among right-leaning actors, with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for analyzing democratic discourse.</abstract>
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%0 Conference Proceedings
%T The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech
%A Rivlin-Angert, Naama
%A Mor-Lan, Guy
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F rivlin-angert-mor-lan-2025-enemy
%X We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from parliamentary speeches (1993-2023), Facebook posts, and leading news outlets (2018-2021), of which 1,812 instances (17.4%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline, and benchmark finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F₁ of 0.74 for binary PDD detection and a macro-F₁ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male politicians than by their female counterparts, and stronger tendencies among right-leaning actors, with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for analyzing democratic discourse.
%R 10.18653/v1/2025.emnlp-main.841
%U https://aclanthology.org/2025.emnlp-main.841/
%U https://doi.org/10.18653/v1/2025.emnlp-main.841
%P 16634-16647
Markdown (Informal)
[The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech](https://aclanthology.org/2025.emnlp-main.841/) (Rivlin-Angert & Mor-Lan, EMNLP 2025)
ACL