用新框架分析2024总统辩论，发现特朗普靠情绪化对抗掌控话语权
Analyzing Biases in Political Dialogue: Tagging U.S. Presidential Debates with an Extended DAMSL Framework
- 扩展DAMSL框架开发BEADS标注体系，捕捉政治对话中的偏见与对抗特征
- 对比人工与零样本大模型标注，发现特朗普在5个维度上显著占优
- 该框架可跨语言跨领域复用，适合研究政治话语中的意识形态操控
我们对2024年美国总统辩论进行了批判性话语分析，聚焦唐纳德·特朗普与乔·拜登、卡玛拉·哈里斯的互动。提出新型标注框架BEADS（Bias Enriched Annotation for Dialogue Structure），系统扩展DAMSL框架以捕捉政治传播中由偏见驱动的对抗性话语特征。BEADS包含与领域和语言无关的标签集，用于建模意识形态框架、情感诉求及对抗策略。方法对比了19,219词的特朗普-拜登辩论与18,123词的特朗普-哈里斯辩论的真人标注与零样本ChatGPT辅助标注结果。分析显示，特朗普在挑战与对抗性互动、选择性强调、恐惧诉求、政治偏见及被感知的轻蔑态度等关键类别中持续占优。这些发现凸显其通过情绪化且对抗性的修辞控制叙事、影响观众认知。本研究确立了BEADS作为跨语言、跨领域、跨政治情境下可扩展、可复现的批判性话语分析框架。
原文摘要 · Abstract (English)
We present a critical discourse analysis of the 2024 U.S. presidential debates, examining Donald Trump's rhetorical strategies in his interactions with Joe Biden and Kamala Harris. We introduce a novel annotation framework, BEADS (Bias Enriched Annotation for Dialogue Structure), which systematically extends the DAMSL framework to capture bias driven and adversarial discourse features in political communication. BEADS includes a domain and language agnostic set of tags that model ideological framing, emotional appeals, and confrontational tactics. Our methodology compares detailed human annotation with zero shot ChatGPT assisted tagging on verified transcripts from the Trump and Biden (19,219 words) and Trump and Harris (18,123 words) debates. Our analysis shows that Trump consistently dominated in key categories: Challenge and Adversarial Exchanges, Selective Emphasis, Appeal to Fear, Political Bias, and Perceived Dismissiveness. These findings underscore his use of emotionally charged and adversarial rhetoric to control the narrative and influence audience perception. In this work, we establish BEADS as a scalable and reproducible framework for critical discourse analysis across languages, domains, and political contexts.
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