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

TL;DR

This study introduces a large Hebrew-language corpus and a two-stage classification model to analyze political delegitimization discourse, revealing its rise over time and variation across platforms and political groups.

Contribution

It presents the first large-scale computational analysis of PDD in Hebrew, including a novel annotated corpus and an effective classification pipeline.

Findings

PDD has increased over three decades.

Higher PDD prevalence on social media than in parliament.

Greater PDD use by male and right-leaning politicians.

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.