  We propose and develop a Lexicocalorimeter: an online, interactive instrument
for measuring the "caloric content" of social media and other large-scale
texts. We do so by constructing extensive yet improvable tables of food and
activity related phrases, and respectively assigning them with sourced
estimates of caloric intake and expenditure. We show that for Twitter, our
naive measures of "caloric input", "caloric output", and the ratio of these
measures are all strong correlates with health and well-being measures for the
contiguous United States. Our caloric balance measure in many cases outperforms
both its constituent quantities, is tunable to specific health and well-being
measures such as diabetes rates, has the capability of providing a real-time
signal reflecting a population's health, and has the potential to be used
alongside traditional survey data in the development of public policy and
collective self-awareness. Because our Lexicocalorimeter is a linear
superposition of principled phrase scores, we also show we can move beyond
correlations to explore what people talk about in collective detail, and assist
in the understanding and explanation of how population-scale conditions vary, a
capacity unavailable to black-box type methods.
