首个英国大选误导性叙事数据集，用于检测选举期虚假信息。
UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections
- 构建首个英美大选误导叙事分类体系
- 整理2019与2024年英国大选中经人工标注的误导性文本
- 评测GPT-4o等模型在识别选举虚假信息上的表现
误导性叙事在选举期间对公众舆论塑造起关键作用，可能影响选民对候选人和政党的认知。为应对这一挑战，本文首次提出欧洲近期选举中常见误导性叙事的分类体系。基于该分类，我们构建并分析了UKElectionNarratives：首个关于2019年与2024年英国大选期间传播的误导性叙事的人工标注数据集。同时，我们对预训练及大语言模型（聚焦GPT-4o）在检测选举相关误导性叙事方面的有效性进行了基准测试。最后，讨论了该数据集的潜在应用场景，并针对未来研究方向提出建议。
原文摘要 · Abstract (English)
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.
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