UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections

TL;DR

This paper introduces a new dataset and taxonomy of misleading narratives from UK elections, benchmarks language models for detection, and discusses future research directions in election misinformation.

Contribution

It presents the first taxonomy and dataset of misleading election narratives in the UK and evaluates language models' effectiveness in detecting them.

Findings

GPT-4o achieves high accuracy in identifying misleading narratives.

The dataset enables systematic analysis of election misinformation.

The taxonomy helps categorize common misleading narratives.

Abstract

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.