分析美国总统辩论中的个人攻击，用大模型识别恶意言论
Analysing Personal Attacks in U.S. Presidential Debates
- 人工标注2016至2024年辩论文本，构建攻击语料库
- 微调Transformer模型与通用大模型，实现攻击检测
- 为媒体与公众提供政治话语透明化分析工具
个人攻击已成为美国总统辩论的显著特征，在选举期间影响公众认知。自动识别此类攻击有助于提升政治话语透明度，为记者、分析师和公众提供洞察。得益于深度学习与基于Transformer的模型（如BERT及大语言模型）的发展，我们提出一个分析美国总统辩论中个人攻击的框架。研究涵盖2016、2020和2024年选举周期的辩论转录文本，进行人工标注，并开展统计分析与语言模型实验。评估了微调后的Transformer模型与通用大语言模型在正式政治演讲中检测个人攻击的能力，验证了特定任务适配现代语言模型对深化政治传播理解的价值。
原文摘要 · Abstract (English)
Personal attacks have become a notable feature of U.S. presidential debates and play an important role in shaping public perception during elections. Detecting such attacks can improve transparency in political discourse and provide insights for journalists, analysts and the public. Advances in deep learning and transformer-based models, particularly BERT and large language models (LLMs) have created new opportunities for automated detection of harmful language. Motivated by these developments, we present a framework for analysing personal attacks in U.S. presidential debates. Our work involves manual annotation of debate transcripts across the 2016, 2020 and 2024 election cycles, followed by statistical and language-model based analysis. We investigate the potential of fine-tuned transformer models alongside general-purpose LLMs to detect personal attacks in formal political speech. This study demonstrates how task-specific adaptation of modern language models can contribute to a deeper understanding of political communication.
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