The Relationship Between Natural Environments and Subjective Well-being as Measured by Sentiment Expressed on Twitter

Abstract

There is growing evidence that time spent in nature can affect well-being. Nonetheless, assessing this relationship can be difficult. We used social media data—1,971,045 geolocated tweets sent by 81,140 users from locations throughout Seattle, Washington, USA—to advance our understanding of the relationship between subjective well-being and natural environments. Specifically, we quantified the relationships between sentiment (negative/neutral/positive) expressed in individual geolocated tweets and their surrounding environments focusing on land-cover type, tree-canopy density, and urban parks. Controlling for multiple covariates such as location types and weather conditions, we estimated three random-intercept partial proportional odds models corresponding to the three environmental indicators. Our results suggest that for a given type of land-use, tweets sent from some natural land-cover types were less likely to be negative compared to tweets sent from the urban-built land-cover type. We also found that for tweets sent in industrial zones, the association between tree-canopy coverage and sentiment polarity was positive: an increase in tree-canopy coverage was associated with a lower probability of having negative sentiments and with a higher probability of having positive sentiments; but for tweets sent in commercial/mixed zones, the association between tree-canopy coverage and sentiment polarity was negative. For parks, tweets sent from urban parks in commercial/mixed zones and residential zones were less likely to be negative compared to tweets sent from outside parks. In industrial zones, only tweets sent from large natural parks (with area >= 40,000 sf2 and impervious surface < 30%) were less likely to be negative. Surprisingly, we also found that tweets sent from large natural parks in residential zones were less likely to be positive compared to tweets sent from outside parks. Geolocated social media data allows nuanced analyses that reveal the complexity of the relationship between subjective well-being and natural environments.