Applying Text Mining and Semantic Network Analysis of Tweets to Investigate Social Perceptions on No Kids Zones in Korea

Abstract

Objectives: This study aims to examine the social perceptions on the ‘No Kids Zones’ considering the continuous spread and the concern of the possibility of increased discrimination against children in our society.Methods: The study used a substantial number of tweets (<i>n</i> = 51,600) with the ‘No Kids Zones’ keyword. First, we probed tweets using Jupyter Notebook to create documents that contain texts between January 2019 and October 2022. Second, we examined TF-IDF, degree centrality, and frequency of words to extract the main keywords and reflect how relevant and connected those words are to a collection of tweet information. Third, based on the 60 keywords we extracted, CONCOR analysis and network visualization were performed using UCINET to examine the co-occurred perceptions on 'No Kids Zones'.Results: The findings demonstrate that the main keywords related to No Kids Zones include kid, parents, adult, children, baby, child and owner in the context of the social target, and ‘cafe’ as social place, and ‘discrimination’, ‘hate’ as social discourse. Second, the analysis showed that there are broad perspectives on children in the context of the No Kids Zones in Korean Society as being either difficult to handle (and, thus, causing inconvenience), or as deserving social understanding and consideration as growing beings.Conclusion: This study suggests a need for improvement both in social understanding and awareness of children and their rights, leading to the prevention of the spread of No Kids Zones. Moreover, parent-focused educational strategies to discipline their children are emphasized for the healthy development of children.