UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections
This addresses the need for accurate detection of misleading narratives in elections to protect public opinion, but it is incremental as it builds on existing datasets and methods for misinformation detection.
The authors tackled the problem of detecting misleading narratives in UK elections by creating the first taxonomy and human-annotated dataset for the 2019 and 2024 general elections, and benchmarked models like GPT-4o for detection, achieving results that show LLMs can perform well but with room for improvement.
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.