A Tool for Spatio-Temporal Analysis of Social Anxiety with Twitter Data

TL;DR

This paper introduces a machine learning tool that analyzes and visualizes the spatio-temporal distribution of social anxiety using Twitter data, aiding understanding of social atmosphere and public opinion.

Contribution

It presents a novel web-based platform that classifies tweets for anxiety and visualizes their distribution over space and time, specifically applied to South Korean Twitter data.

Findings

Effective classification of anxious tweets achieved

Visualizations reveal social anxiety patterns over time and space

Demonstrated usefulness in exploring social atmosphere in South Korea

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.