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Mapping Social Distress: A Computational Approach to Spatiotemporal Distribution of Anxiety

Title
Mapping Social Distress: A Computational Approach to Spatiotemporal Distribution of Anxiety
Author
최용석
Keywords
anxiety; spatiotemporal distribution; machine learning; social media; computational social sciences
Issue Date
2020-04
Publisher
SAGE PUBLICATIONS INC
Citation
SOCIAL SCIENCE COMPUTER REVIEW, article no. 0894439320914505
Abstract
Anxiety is a pervasive emotional state that tends to arise in situations involving uncertainty due partly to social and contextual issues including competition, economic disparity, and social insecurity. Thus, distribution of aggregate emotions, such as in anxiety, may reveal an important picture of otherwise invisible social processes in which individuals interact with local and global opportunities, constraints, and potential threats. The aim of this study is to present a computational approach to the dynamic distribution of anxiety extracted from natural language expressions of users of Twitter, a popular global social media platform. We develop an unsupervised machine learning procedure based on a naive Bayes model to classify contents of anxiety, estimate the degree of anxiety, and construct a geographic map of spatiotemporal distribution of anxiety. To validate our mapping results, a multilevel statistical analysis was performed to examine how anxiety distribution is correlated with other district-level sociodemographic statistics such as rates of birth and early divorce. Implications for further research and extension are discussed.
URI
https://journals.sagepub.com/doi/10.1177/0894439320914505https://repository.hanyang.ac.kr/handle/20.500.11754/166080
ISSN
0894-4393; 1552-8286
DOI
10.1177/0894439320914505
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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