The dissemination of fear: tracking anti-vaccine sentiment with network analysis and visualisation
Ms Kim Doyle1, Dr Daniel Russo-Batterham1
1University of Melbourne, Melbourne, Australia
Previous years have seen public trust in mainstream media and politicians decline. At the same time, the public is increasingly sourcing news from social media platforms and other alternative sources that are not subject to the same regulation as media companies. As vaccinations have progressed around the world, health authorities have had to battle a deluge of unbalanced and negative claims about the effects and efficacy of various vaccines. There has been a lot of discussion about the best communication strategy to overcome vaccine hesitancy.
In this paper, we explore the use of network analysis and visualisation as techniques for better understanding the dissemination of anti-vaccine content online through a close study of Twitter and Facebook data. We focus on the shifting sentiment toward AstraZeneca and other vaccines in Australia. Network graphs provide useful overviews of how far and how quickly information spreads across social media. Visualising these networks over time can reveal causal relationships between specific events and public attitudes and anxieties. Combining network analysis and visualisation techniques with sentiment analysis will help to measure the extent to which patterns of dissemination across social media are positive or negative. Finally, we examine both analytic and computational limitations of the techniques presented, and suggest possible future improvements.
From 2011 to 2013, Dr Daniel Russo-Batterham worked as a researcher at the Centre d’Études Supérieures de la Renaissance in Tours, France, while completing a Master of Music. Since graduating from his PhD in 2018, Daniel has worked on Digital Humanities projects across Australia and abroad. He has a background in python, data wrangling, relational database design, web scraping, quantitative methods, natural language processing, and a broad range of approaches to visualisation. He is currently working in the Melbourne Data Analytics Platform.
Kim Doyle is a Research Data Specialist at the Melbourne Data Analytics Platform (MDAP) and a PhD in Media and Communications at the University of Melbourne. Previously, she taught natural language processing and data mining to researchers at the University of Melbourne’s Research Platform Services at the for a number of years. Her research interests include political communication, social media and computational social science.