Misleading narratives play a crucial role in shaping public opinion during elections, as they can influence how voters perceive candidates and political parties. This entails the need to detect these narratives accurately. To address this, we introduce the first taxonomy of common misleading narratives that circulated during recent elections in Europe. Based on this taxonomy, we construct and analyse UKElectionNarratives: the first dataset of human-annotated misleading narratives which circulated during the UK General Elections in 2019 and 2024. We also benchmark Pre-trained and Large Language Models (focusing on GPT-4o), studying their effectiveness in detecting election-related misleading narratives. Finally, we discuss potential use cases and make recommendations for future research directions using the proposed codebook and dataset.

本研究针对选举期间影响公众舆论的误导性叙事，提出了对这一领域的首个分类体系，并构建了2019年和2024年英国大选期间人类注释的误导性叙事数据集UKElectionNarratives。本研究的主要发现是，利用预训练和大型语言模型（特别是GPT-4o）进行相关叙事检测具有重要的应用潜力，为未来研究提供了新的方向。

英国选举叙事：围绕近期英国大选的误导性叙事数据集