An Unobtrusive Behavioral Model of “Gross National Happiness”

ACM Conference on Human Factors in Computing Systems (CHI)

Abstract

I analyze the use of emotion words for approximately 100 million Facebook users since September of 2007. “Gross national happiness” is operationalized as a standardized difference between the use of positive and negative words, aggregated across days, and present a graph of this metric.

I begin to validate this metric by showing that positive and negative word use in status updates covaries with self-reported satisfaction with life (convergent validity), and also note that the graph shows peaks and valleys on days that are culturally and emotionally significant (face validity).

I discuss the development and computation of this metric, argue that this metric and graph serves as a representation of the overall emotional health of the nation, and discuss the importance of tracking such metrics.

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