首次用算法分析以色列政治话语中的去合法化言论，发现其30年持续上升。
The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech
- 构建1.04万句希伯来语语料库，结合大模型与微调模型识别去合法化言论
- 发现去合法化言论占比17.4%，社交平台比议会发言更常见，右翼和男性政客更频繁使用
- 可自动检测言论强度与情感框架，适合研究民主话语与政治极化
我们首次开展大规模计算研究，分析政治去合法化话语（PDD），即对政治主体规范合法性进行符号性攻击的言论。研究构建并人工标注了包含10,410句的希伯来语语料库，涵盖1993-2023年议会发言、2018-2021年脸书帖子及主流新闻媒体内容，其中1,812例（17.4%）呈现PDD，并对642例额外标注了强度、不文明程度、目标类型与情感框架。提出两阶段分类流程，结合微调编码器与解码大模型，最佳模型DictaLM 2.0在二元PDD检测中达到F₁=0.74，特征分类宏F₁=0.67。应用该模型分析跨平台与纵向数据，发现三十年间PDD显著上升，社交平台高于议会辩论，男性政客使用更多，右翼倾向更强，选举与重大政治事件期间尤为突出。结果表明自动化分析在理解民主话语方面具有可行性与价值。
原文摘要 · Abstract (English)
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.
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