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.

翻译：我们首次对政治合法性剥夺话语进行了大规模计算研究，该话语被定义为对政治实体规范有效性的象征性攻击。我们整理并人工标注了一个新颖的希伯来语语料库，包含10,410个句子，来源涵盖以色列议会演讲（1993-2023年）、Facebook帖子（2018-2021年）以及主要新闻媒体。其中1,812个实例（17.4%）表现出政治合法性剥夺话语特征，另有642个实例额外标注了强度、非文明程度、目标类型和情感框架。我们引入了一个结合微调编码器模型与解码器大语言模型的两阶段分类流程。我们的最佳模型（DictaLM 2.0）在二元政治合法性剥夺话语检测中达到0.74的F$_1$值，在合法性剥夺特征分类中达到0.67的宏平均F$_1$值。将该分类器应用于纵向和跨平台数据分析，我们发现：三十年间政治合法性剥夺话语显著增加，社交媒体上的出现频率高于议会辩论，男性政治人物比女性更常使用，右翼倾向行为者表现出更强倾向性——在选举活动和重大政治事件期间出现明显峰值。我们的研究结果证明了自动化政治合法性剥夺话语分析对于理解民主话语的可行性与价值。