UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections - Haouari, Fatima; Scarton, Carolina; Faggiani, Nicolò; Nikolaidis, Nikolaos; Kotseva, Bonka; Abu Farha, Ibrahim; Linge, Jens; Bontcheva, Kalina (Proceedings of the International AAAI Conference on Web and Social Media, 2025)
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 needto 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 thistaxonomy, 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.