“Strong minds discuss ideas, average minds discuss events, weak minds discuss people.”
-Socrates
My daughter has pointed out to me that I spend too much time on Twitter and to emphasize the point she bought me a Twitter addict mug. She urged me to keep to science and big ideas rather than science gossip. Rather than argue that she is unfairly characterizing the way I keep up with developments in the world of science and medicine, I decided that the best defense would be to turn to analyzing tweets and make my Twitter habit a programming project. As my timeline was filled up with tweets about COVID-19, I’ve decided to focus on tweets about the disease and virus. This has afforded me the opportunity to brush up on the Tidyverse and start to sharpen my data science tools for this remarkable, surely biased and yet highly informative stream of messages from around the world about this pandemic, now rounding out its 1st year. This is only part 1. I left the other parts on the cutting floor so that I could quickly check to see if my prior expectations about content were correct (spoiler: there were not).
This hyperlink will bring you to an HTML file with the first version of the Part 1 analysis. I’ll be providing additional versions as I find time to steal from research, teaching and research supervision. Suggestions on presentation or analyses are much appreciated. Comments and critique are welcome too.