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.
View on arXiv@article{haouari2025_2505.05459,
title={ UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections },
author={ Fatima Haouari and Carolina Scarton and Nicolò Faggiani and Nikolaos Nikolaidis and Bonka Kotseva and Ibrahim Abu Farha and Jens Linge and Kalina Bontcheva },
journal={arXiv preprint arXiv:2505.05459},
year={ 2025 }
}