The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech

• URL: http://arxiv.org/abs/2508.15524v2
• Date: Mon, 13 Oct 2025 11:56:31 GMT
• Title: The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech
• Authors: Naama Rivlin-Angert, Guy Mor-Lan,
• Abstract summary: We present the first large-scale computational study of political delegitimization discourse (PDD)<n>PDD is symbolic attacks on the normative validity of political entities.<n>We use a novel Hebrew-language corpus of 10,410 sentences drawn from Knesset speeches.
• Score: 0.21485350418225244
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Knesset speeches (1993-2023), Facebook posts (2018-2021), and leading news outlets, 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 combining 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 than female politicians, 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 understanding democratic discourse.

Related papers

• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• Who Attacks, and Why? Using LLMs to Identify Negative Campaigning in 18M Tweets across 19 Countries [0.0]
  This study introduces zero-shot Large Language Models as a novel approach for cross-lingual classification of negative campaigning.<n>Using benchmark datasets in ten languages, we demonstrate that LLMs achieve performance on par with native-speaking human coders.<n>Second, we leverage this novel method to conduct the largest cross-national study of negative campaigning to date.
  arXiv  Detail & Related papers  (2025-07-23T16:02:52Z)
• Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models [72.89977583150748]
  We propose a novel methodology to assess how Large Language Models align with broader geopolitical value systems.<n>We find that LLMs generally favor democratic values and leaders, but exhibit increases favorability toward authoritarian figures when prompted in Mandarin.
  arXiv  Detail & Related papers  (2025-06-15T07:52:07Z)
• Affective Polarization Amongst Swedish Politicians [0.0]
  This study investigates affective polarization among Swedish politicians on Twitter from 2021 to 2023.<n>Negative partisanship becomes significantly more dominant when the in-group is defined at the party level.<n>Negative partisanship also proves to be a strategic choice for online visibility, attracting 3.18 more likes and 1.69 more retweets on average.
  arXiv  Detail & Related papers  (2025-03-20T14:40:48Z)
• On the Use of Proxies in Political Ad Targeting [49.61009579554272]
  We show that major political advertisers circumvented mitigations by targeting proxy attributes. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
  arXiv  Detail & Related papers  (2024-10-18T17:15:13Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• Changes in Policy Preferences in German Tweets during the COVID Pandemic [4.663960015139793]
  We present a novel data set of tweets with fine grained political preference annotations. A text classification model trained on this data is used to extract political opinions. Results indicate that in response to the COVID pandemic, expression of political opinions increased.
  arXiv  Detail & Related papers  (2023-07-31T16:07:28Z)
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
  We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.<n>We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
  arXiv  Detail & Related papers  (2023-04-19T18:32:49Z)
• Computational Assessment of Hyperpartisanship in News Titles [55.92100606666497]
  We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection. Overall the Right media tends to use proportionally more hyperpartisan titles. We identify three major topics including foreign issues, political systems, and societal issues that are suggestive of hyperpartisanship in news titles.
  arXiv  Detail & Related papers  (2023-01-16T05:56:58Z)
• Hate versus Politics: Detection of Hate against Policy makers in Italian tweets [0.6289422225292998]
  This paper addresses the issue of classification of hate speech against policy makers from Twitter in Italian. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and anti-immigration domains.
  arXiv  Detail & Related papers  (2021-07-12T12:24:45Z)
• Political Posters Identification with Appearance-Text Fusion [49.55696202606098]
  We propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters. The majority of this work focuses on political posters that are designed to serve as a promotion of a certain political event.
  arXiv  Detail & Related papers  (2020-12-19T16:14:51Z)
• Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections [4.560033258611709]
  We present the first publicly open dataset for detecting specific text chunks and categories of political advertising in the Polish language. It contains 1,705 human-annotated tweets tagged with nine categories, which constitute campaigning under Polish electoral law.
  arXiv  Detail & Related papers  (2020-06-17T23:58:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.