Using Twitter data to provide qualitative insights into pandemics and epidemics

Background: One area of public health research specialises in examining public views and opinions surrounding infectious disease outbreaks. Although interviews and surveys are valid sources of this information, views and opinions are necessarily generated by the context, rather than spontaneous. As such, social media has increasingly been viewed as legitimate source of pragmatic, unfiltered public opinion. Objectives: This research attempts to better understand how users converse about infectious disease outbreaks on the social media platform Twitter. The study was undertaken in order to address a gap in knowledge because previous empirical studies that have analysed infectious disease outbreaks on Twitter have focused on employing quantitative methods as the primary form of data analysis. After analysing individual cases on Ebola, Zika, and swine flu, the study performs an important comparison in the types of discussions taking place on Twitter and is the first empirical study to do so. Methods: A number of pilot studies were initially designed and conducted in order to help inform the main study. The study then manually labels tweets on infectious disease outbreaks assisted by the qualitative analysis programme NVivo, and performs an analysis using the Health Belief Model, concepts around information theory, and a number of sociological principles. The data were purposively sampled according to when Google Trends Data showed a heightened interest in the respective outbreaks, and a case study approach was utilised. Results: A substantial number of themes were uncovered which were not reported in previous literature, demonstrating the potential of qualitative methodologies for extracting greater insight into public health opinions from Twitter data. The study noted several limitations of Twitter data for use in qualitative research. However, results demonstrated the potential of Twitter to identify discussions around infectious diseases that might not emerge in an interview and/or which might not be included in a survey.