Measuring the Happiness, Health, and Stories of Populations Peter Dodds University of Vermont Department of Mathematics and Statistics In this talk, I will present our work on building what we call "lexical meters"---online instruments that use social media and other texts to quantify population rates of a wide array of human behaviour such as wealth, exercise levels, obesity rates, and sleep insufficiency. I will first showcase our hedonometer, an instrument for measuring positivity in written expression. I'll show how we have consistently improved our methods to allow us to explore collective, dynamical patterns of happiness found in massive text corpora including Twitter, song lyrics, works of literature, movies, political speeches, and news sources. I will present evidence for how 10 diverse natural languages appear to contain a striking frequency-independent positive bias, describing how this phenomenon plays a key role in our instrument's performance, and how it may more deeply reflects human nature. I will then discuss our work on building the Panometer, introducing our latest instrument: the Lexicocalorimeter, a principled meter that turns phrases into calories. If time allows, I will point to a number other diverse projects being carried by our team in the Computational Story Lab, ranging from the stories of sports to the dynamics of climate change.