New Artificial Intelligence Analyzes Tweets Debating Vaccination and Climate Change

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. 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. Only some 15 or 20 percent of users expressed a pro-vaccine sentiment, while around 70 percent 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.

“It is an open question whether these differences in user sentiment and social media echo chambers concerning vaccines created the conditions for highly polarized vaccine sentiment when the COVID-19 vaccines began to roll out,” said Chris Bauch, professor of applied mathematics at the University of Waterloo.

The research goal was 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.

Vaccination is a much hotter topic right now and appears to be much more polarized given the ongoing pandemic. The study is published in the journal Humanities and Social Sciences Communications.
Most other research looks at these as isolated issues, but we wanted to look at these two issues of climate change and vaccination side-by-side. Both issues have social and environmental components, and there is a lot to learn in this research pairing.

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. The time range for the tweets is between 2007 and 2016.

This means that the data precedes COVID-19 and offers a snapshot of vaccine sentiment in the years leading up to the pandemic.

The AI ranked the millions of tweets as either pro, anti or neutral sentiment on the issues and then classified users in pro, anti or neutral categories. It also analyzed the structure of online communities and the degree to which users with opposing sentiments interacted.

Source: Medindia

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