Temporal patterns of happiness and information in a global social network

Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter

Abstract

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quanti able economic indicators, such as gross domestic product. Here, we use a real-time, remote-sensing, noninvasive, text-based approach|a kind of hedonometer|to uncover collective dynamical patterns of happiness levels expressed by over 50 million users in the online, global social network Twitter. With a data set comprising nearly 2.8 billion expressions involving more than 28 billion words, we explore temporal variations in happiness, as well as information levels, over time scales of hours, days, and months. Among many observations, we nd a steady global happiness level, evidence of universal weekly and daily patterns of happiness and information, and that happiness and information levels are generally uncorrelated. We also extract and analyse a collection of happiness and information trends based on keywords, showing them to be both sensible and informative, and in effect generating opinion polls without asking questions. Finally, we develop and employ a graphical method that reveals how individual words contribute to changes in average happiness between any two texts.

It takes a little bit of time to make sense of the graphs . . . but it is worthy . . . just imagine the future . . .

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