Dataset Information

Happiness and the patterns of life: a study of geolocated tweets.

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 have 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.

SUBMITTER: Frank MR

PROVIDER: S-EPMC6505625 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Happiness and the patterns of life: a study of geolocated tweets.

Frank Morgan R MR Mitchell Lewis L Dodds Peter Sheridan PS Danforth Christopher M CM

Scientific reports 20130101

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 have 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 an ...[more]

PMID: 24026340

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Project description:Billions of users of mobile phones, social media platforms, and other technologies generate an increasingly large volume of data that has the potential to be leveraged towards solving public health challenges. These and other big data resources tend to be most successful in epidemiological applications when utilized within an appropriate conceptual framework. Here, we demonstrate the importance of assumptions about host mobility in a framework for dynamic modeling of infectious disease spread among districts within a large urban area. Our analysis focused on spatial and temporal variation in the transmission of dengue virus (DENV) during a series of large seasonal epidemics in Lahore, Pakistan during 2011-2014. Similar to many directly transmitted diseases, DENV transmission occurs primarily where people spend time during daytime hours, given that DENV is transmitted by a day-biting mosquito. We inferred spatiotemporal variation in DENV transmission under five different assumptions about mobility patterns among ten districts of Lahore: no movement among districts, movement following patterns of geo-located tweets, movement proportional to district population size, and movement following the commonly used gravity and radiation models. Overall, we found that inferences about spatiotemporal variation in DENV transmission were highly sensitive to this range of assumptions about intra-urban human mobility patterns, although the three assumptions that allowed for a modest degree of intra-urban mobility all performed similarly in key respects. Differing inferences about transmission patterns based on our analysis are significant from an epidemiological perspective, as they have different implications for where control efforts should be targeted and whether conditions for transmission became more or less favorable over time.Electronic supplementary materialThe online version of this article (10.1140/epjds/s13688-018-0144-x) contains supplementary material.

Project description:BackgroundDespite having one of the highest Gross Domestic Product (GDP) per capita levels, United States (US) ranks lower in subjective well-being, including happiness and life satisfaction, compared with European countries. Studies of the impact of happiness and life satisfaction on life expectancy and mortality in the US are limited or non-existent. Using a national longitudinal dataset, we examined the association between levels of happiness/life satisfaction and US life expectancy and all-cause mortality.MethodsWe analyzed the 2001 National Health Interview Survey (NHIS) prospectively linked to 2001-2014 mortality records in the National Death Index (NDI) (N=30,377). Cox proportional hazards regression was used to model survival time as a function of happiness, life satisfaction, and sociodemographic and behavioral covariates.ResultsLife expectancies at age 18 among adults with high levels of happiness and life satisfaction were, respectively, 7.5 and 8.9 years higher compared to those with low levels of happiness and life satisfaction. In Cox models with 14 years of mortality follow-up, all-cause mortality risk was 82% higher (hazard ratio [HR]=1.82; 95% CI=1.59,2.08) in adults with little or no happiness, controlling for age, and 36% higher (HR=1.36; 95% CI=1.17,1.57) in adults with little/no happiness, controlling for sociodemographic, behavioral and health characteristics, when compared with adults reporting happiness all of the time. Mortality risk was 107% higher (HR=2.07; 95% CI=1.80,2.38) in adults who were very dissatisfied with their life, controlling for age, and 39% higher (HR=1.39; 95% CI=1.20,1.60) in adults who were very dissatisfied, controlling for all covariates, when compared with adults who were very satisfied.Conclusions and global health implicationsAdults with higher happiness and life satisfaction levels had significantly higher life expectancy and lower all-cause mortality risks than those with lower happiness and satisfaction levels. These findings underscore the significance of addressing subjective well-being in the population as a strategy for reducing all-cause mortality.

Dataset Information

Happiness and the patterns of life: a study of geolocated tweets.

Publications

Happiness and the patterns of life: a study of geolocated tweets.

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