The research project set out to learn how sentiments on climate change and vaccination may be related, how users form networks and share information, the relationship between online sentiments, and how people act and make decisions in daily life.
The team trained a machine-learning algorithm to analyze a massive trove of tweets about climate change and vaccination within the time frame between 2007 and 2016. The dataset for the project was drawn from a few sources, including some that were purchased from Twitter. In total, the analysis takes into consideration roughly 87 million tweets.
Results showed that climate change sentiment was overwhelmingly on the pro side of those that believe climate change is happening because of human activity and requires action. There was also a significant amount of interaction between users with opposite sentiments about climate change.
However, in the snapshot of the timeframe of the dataset, vaccine sentiment was nowhere near so uniform. In fact, only some 15 or 20 per cent of users expressed a clearly pro-vaccine sentiment, while around 70 per cent expressed no strong sentiment. Perhaps more importantly, individuals and entire online communities with differing sentiments toward vaccination interacted much less than the climate change debate.
The research team is led by applied mathematics professor Chris Bauch and University of Guelph professor of environmental studies Madhur Anand. Bauch and Anand are regular research collaborators and also recently published work on AI and climate change tipping points. The team also included Waterloo- and Guelph-affiliated researchers Jeffrey Cheng, Edward Qian, Justin Schonfeld and Jason Sinn.
Read more in the article on Waterloo News.