Crisis talk: analysis of the public debate around the energy crisis and cost of living

TL;DR

This study develops a scalable NLP-driven pipeline to analyze UK newspaper discourse on the energy crisis and cost of living (2014–2023), combining metadata-based retrieval, Autophrase phrase mining, LDA topic modeling, and semantic-role sentiment analysis to identify topics, actors, and their roles. It finds five main discourse themes and demonstrates varying framing across newspapers, with cost-of-living as a dominant lens and climate policy as a solution-oriented thread. The work provides a reproducible methodological framework and visual analytics for cross-source discourse analysis, offering practical insights for policy, communication, and further research into energy, poverty, and risk narratives.

Abstract

A prominent media topic in the UK in the early 2020s is the energy crisis affecting the UK and most of Europe. It brings into a single public debate issues of energy dependency and sustainability, fair distribution of economic burdens and cost of living, as well as climate change, risk, and sustainability. In this paper, we investigate the public discourse around the energy crisis and cost of living to identify how these pivotal and contradictory issues are reconciled in this debate and to identify which social actors are involved and the role they play. We analyse a document corpus retrieved from UK newspapers from January 2014 to March 2023. We apply a variety of natural language processing and data visualisation techniques to identify key topics, novel trends, critical social actors, and the role they play in the debate, along with the sentiment associated with those actors and topics. We combine automated techniques with manual discourse analysis to explore and validate the insights revealed in this study. The findings verify the utility of these techniques by providing a flexible and scalable pipeline for discourse analysis and providing critical insights for cost of living - energy crisis nexus research.

Figures (8)

• Figure 1: The NLP pipeline.
• Figure 2: Topics learned across four sources
• Figure 3: Issues across all four sources.
• Figure 4: The 'cost-of-living' issue and actors associated with it.
• Figure 5: The 'cost-of-living-crisis' issue and actors associated with it.