Examining shifts in public discourse on electric mobility adoption through Twitter data

Abstract

Most of the past research on public attitudes or choices on electric vehicles (EVs) have used user survey data (i.e., at a microscopic level). This paper adopts a multi-method approach to conduct a data-driven exploration of public sentiment evolution using a public tweet database spread across the last decade (2012–2022) (i.e., at a macroscopic level). Data mining from Twitter has enabled this research to obtain a rich alternative source of public sentiments on EVs. Natural Language Processing (NLP) techniques were then used to analyse the evolution in sentiments and reveal the underlying patterns in the discourse on EVs. Results and discussions in this paper categorise the shifts in public opinion and recognise critical linkages of economic summits or global events to sentiment changes. The research outcomes are expected to offer a strategic vantage point on the evolution of public discourse related to EV adoption and assist in designing better transportation electrification policies. © 2023 Elsevier Ltd