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Understanding happiness in cities using Twitter: Jobs, children, and transport
The demographics and landscape of cities are changing rapidly, and there is an emphasis to better understand the factors which influence citizen happiness in order to design smarter urban systems. Few studies have attempted to understand how large-scale sentiment maps to urban human geography. Inferring sentiment from social media data is one such scalable solution. In this paper, we apply natural language processing (NLP) techniques to 0.4 million geo-tagged Tweets in the Greater London area to understand the influence of socioeconomic and urban geography parameters on happiness. Our results not only verify established thinking: that job opportunities correlate with positive sentiments; but also reveal two insights: (1) happiness is negatively correlated with number of children, and (2) happiness has a U-shaped (parabolic) relationship with access to public transportation. The latter implies that the happiest people are those who have good access to public transport, or such poor access that they use private transportation. The number of jobs, children, and transportation availability are every day facets of urban living and individually account for up to 47% of the variations in people's happiness. Our results show that they influence happiness more significantly than long term socioeconomic parameters such as degradation, education, income, housing, and crime. This study will enable urban planners and system designers to move beyond the traditional cost-benefit methodology and to incorporate citizens' happiness.
Understanding happiness in cities using Twitter: Jobs, children, and transport
The demographics and landscape of cities are changing rapidly, and there is an emphasis to better understand the factors which influence citizen happiness in order to design smarter urban systems. Few studies have attempted to understand how large-scale sentiment maps to urban human geography. Inferring sentiment from social media data is one such scalable solution. In this paper, we apply natural language processing (NLP) techniques to 0.4 million geo-tagged Tweets in the Greater London area to understand the influence of socioeconomic and urban geography parameters on happiness. Our results not only verify established thinking: that job opportunities correlate with positive sentiments; but also reveal two insights: (1) happiness is negatively correlated with number of children, and (2) happiness has a U-shaped (parabolic) relationship with access to public transportation. The latter implies that the happiest people are those who have good access to public transport, or such poor access that they use private transportation. The number of jobs, children, and transportation availability are every day facets of urban living and individually account for up to 47% of the variations in people's happiness. Our results show that they influence happiness more significantly than long term socioeconomic parameters such as degradation, education, income, housing, and crime. This study will enable urban planners and system designers to move beyond the traditional cost-benefit methodology and to incorporate citizens' happiness.
Understanding happiness in cities using Twitter: Jobs, children, and transport
Guo, Weisi (author) / Gupta, Neha (author) / Pogrebna, Ganna (author) / Jarvis, Stephen (author)
2016-09-01
6022054 byte
Conference paper
Electronic Resource
English
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