We have studied social media discussion of so-called migrant caravans, with a particular emphasis on how public opinion leaders have shaped these discussions. Building on this, we have broadened our investigation to the issue of misinformation about immigration more generally, investigating several false claims about immigration and their spread through the social media sphere. Here we use topic modeling to home in on specific claims and then use collocation and network analysis tools to investigate the spread of these claims.
In analyzing discussions of pandemics, we compare how people talk about COVID-19 in different languages: what are the most common aspects people discuss? Which other countries do they refer to most often? And in particular, how do people in different countries (and languages) refer to China as the country where COVID-19 was first identified in humans? This work involves applying automated translation tools to large volumes of Tweets and then identifying key patterns using techniques such as sentiment analysis, the detection of emotion in text, and topic modeling.