Happiness and the Patterns of Life: A Study of Geolocated Tweets

TL;DR

This study analyzes 37 million geolocated tweets to explore how individuals' expressed happiness varies with their movement patterns, revealing that happiness increases logarithmically with distance from their typical location.

Contribution

It introduces a novel approach combining high-resolution geolocation data with sentiment analysis to study happiness patterns related to movement.

Findings

Happiness increases logarithmically with distance from average location.

High spatial resolution data improves understanding of movement and sentiment.

Large-scale analysis of social media reveals new insights into happiness and mobility.

Abstract

The patterns of life exhibited by large populations have been described and modeled both as a basic science exercise and for a range of applied goals such as reducing automotive congestion, improving disaster response, and even predicting the location of individuals. However, these studies previously had limited access to conversation content, rendering changes in expression as a function of movement invisible. In addition, they typically use the communication between a mobile phone and its nearest antenna tower to infer position, limiting the spatial resolution of the data to the geographical region serviced by each cellphone tower. We use a collection of 37 million geolocated tweets to characterize the movement patterns of 180,000 individuals, taking advantage of several orders of magnitude of increased spatial accuracy relative to previous work. Employing the recently developed sentiment analysis instrument known as the 'hedonometer', we characterize changes in word usage as a function of movement, and find that expressed happiness increases logarithmically with distance from an individual's average location.