Title: Questioning Vaccination Discourse (Quo VaDis): A Corpus-Based Study
Funder: Economic and Social Research Council

Abstract: The overarching objective of the Quo VaDis project is to help understand and address the national and global health threat of vaccine hesitancy by providing in-depth, systematic, diachronic linguistic accounts of the ways in which views about vaccination are expressed in UK public discourse and online discourse in English, using the cutting-edge techniques for large-scale computer-aided linguistic analysis associated with Corpus Linguistics. The more specific objectives are: 1. To investigate the variety of ways in which views about vaccinations are expressed and justified in three multi-million-word corpora consisting of: a. English-language contributions to three social media platforms: Twitter, Reddit and Mumsnet, since the inception of each platform - respectively, 2006, 2005 and 2000; b. UK national newspapers since 1990; and c. UK parliamentary debates since 1830. 2. To provide detailed evidence of: i. The evolution over time of arguments and debates on vaccinations, including in terms of the propagation and changes in popularity of different views; ii. Similarities, differences, mutual influences and interactions among discussions of vaccinations reflecting different views, within and across public discourse domains; iii. Relationships between views about vaccinations and statements about vaccination-related decisions, including past, future or hypothetical decisions, and particularly decisions about children. 3. To work with the Project Partners (Public Health England, the Department of Health and Social Care and the Department for Digital, Culture, Media & Sport) and other interested stakeholders to use the findings of the research to inform the design of future initiatives and messages aimed at reducing vaccine hesitancy, for example by producing messages tailored (in terms of style, argument, sources of evidence/authority, etc.) to vaccine hesitant groups that may be most amenable to persuasion.

Lay abstract: The Quo VaDis project applies the latest techniques for large-scale computer-aided linguistic analysis to discussions about vaccinations in public discourse, and specifically in: social media discussions in English, UK Parliamentary debates and UK national press reports. The goal is to arrive at a better understanding of pro- and anti-vaccination views, as well as undecided views, which will inform future public health campaigns. The project will be based in the world-renowned ESRC Centre for Corpus Approaches to Social Science (CASS) at Lancaster University, which was awarded a Queen's Anniversary Prize for Higher and Further Education in 2015. An interdisciplinary project team will work in interaction with three main project partners: Public Health England, the Department of Health and Social Care and the Department for Digital, Culture, Media & Sport. The World Health Organization's (WHO) list of top ten global health threats includes 'vaccine hesitancy' - 'a delay in acceptance or refusal of vaccines despite availability of vaccination services'. Vaccination programmes are currently estimated to prevent between 2 and 3 million deaths a year worldwide. However, uptake of vaccinations in 90% of countries has been reported to be affected by vaccine hesitancy. In England, coverage for all routine childhood vaccinations is in decline, resulting in the resurgence of communicable diseases that had previously been eradicated. In August 2019, the UK lost its WHO measles elimination status. The reasons for vaccine hesitancy are complex, but they need to be understood in order to be addressed effectively. This project focuses on discourse because the ways in which controversial topics such as vaccinations are talked about both reflect and shape beliefs and attitudes, which may in turn influence behaviour. More specifically, vaccinations have been the topic of UK parliamentary debates since before the first Vaccination Act of 1840; they have been increasingly discussed in the UK press since the early 1990s; and anti-vaccination views in particular have been described as part of a complex network of 'anti-public discourses' which, in recent years, are known to be both spread and contested on social media. This project will involve the analysis of three multi-million-word datasets: (1) English-language contributions to three social media platforms: Mumsnet, Reddit and Twitter since the inception of each platform - respectively, 2000, 2005 and 2006; (2) UK national newspapers since 1990; and (3) UK parliamentary debates since 1830. These datasets will be analysed in a data-driven fashion by means of the computer-aided methods associated with Corpus Linguistics - a branch of Linguistics that involves the construction of large digital collections of naturally-occurring texts (known as 'corpora') and their analysis through tailor-made software. A corpus linguistic approach makes it possible to combine in a principled way the quantitative analysis of corpora containing millions of words with the qualitative analysis of individual texts, patterns and interactions. In this way, we will identify and investigate the different ways in which views about vaccinations are expressed in our data, for example, through patterns in choices of vocabulary, pronouns, negation, evaluation, metaphors, narratives, sources of evidence, and argumentation. We will reveal both differences and similarities in pro- and anti-vaccination views over time and across different groups of people, particularly as they form and interact on social media. Our findings will make a major contribution to an understanding of views about vaccinations both in the UK (via our parliamentary and news datasets) and internationally (via our social media datasets). Through the involvement of our Project Partners, as well as more general engagement activities, these findings will be used as evidence for the design of future public health campaigns about vaccinations.

Research activity codes: 3.4 VACCINES
Health categories: INFECTION