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Measuring Happiness and Health with Social Media Data

About the Presentation: Our group builds socio-technical instruments: novel algorithms for measuring the emotional and physical health of individuals and populations. These algorithms are meant to function very much in the manner of physical instruments like the telescope or thermometer, but with the aim of quantifying important and previously poorly resolved social phenomenon. This talk will showcase a series of our studies which use online content to quantify happiness and physical health at the population scale, as well as mental health problems in individuals. It will feature our Hedonometer and its collective patterns of happiness found in 100 billion tweets, as well as our recent study showing Instagram photos reveal predictive markers for depression.

 About the Presenter:  Chris Danforth co-directs the Computational Story Lab, a group of applied mathematicians at the undergraduate, masters, phd, and postdoctoral level working on large-scale, system problems in many fields including sociology, nonlinear dynamics, networks, ecology, and physics. Danforth’s background is in the application of chaos theory to weather & climate prediction. His current work is in Computational Social Science, exploring human behavior through social media data. Danforth is the co-inventor of, a socio-technical instrument measuring daily happiness based on 100 billion Twitter messages. He has also developed algorithms to identify predictors of depression from Instagram photos. His research has been covered by the New York Times, Science Magazine, and the BBC among others. Descriptions of his projects are available at his website: