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A tool for spatio-temporal analysis of social anxiety with Twitter data

Title
A tool for spatio-temporal analysis of social anxiety with Twitter data
Author
손동영
Keywords
SNS (Social Networking Service); Spatio-Temporal Information; Machine Learning; Naïve Bayes Classifier; Visualization
Issue Date
2019-04
Publisher
Association for Computing Machinery
Citation
Proceedings of the ACM Symposium on Applied Computing, Page. 2120-2123
Abstract
In this paper, we present a tool for analyzing spatio-temporal distribution of social anxiety. Twitter, one of the most popular social network services, has been chosen as data source for analysis of social anxiety. Tweets (posted on the Twitter) contain various emotions and thus these individual emotions reflect social atmosphere and public opinion, which are often dependent on spatial and temporal factors. The reason why we choose anxiety among various emotions is that anxiety is very important emotion that is useful for observing and understanding social events of communities. We develop a machine learning based tool to analyze the changes of social atmosphere spatially and temporally. Our tool classifies whether each Tweet contains anxious content or not, and also estimates degree of Tweet anxiety. Furthermore, it also visualizes spatio-temporal distribution of anxiety as a form of web application, which is incorporated with physical map, word cloud, search engine and chart viewer. Our tool is applied to a big tweet data in South Korea to illustrate its usefulness for exploring social atmosphere and public opinion spatio-temporally.
URI
https://dl.acm.org/citation.cfm?doid=3297280.3297619https://repository.hanyang.ac.kr/handle/20.500.11754/110827
ISBN
978-1-4503-5933-7
DOI
10.1145/3297280.3297619
Appears in Collections:
COLLEGE OF SOCIAL SCIENCES[S](사회과학대학) > MEDIA COMMUNICATION(미디어커뮤니케이션학과) > Articles
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