Network analysis based on big data in social media of Korean adolescents’ diet behaviors

Abstract

Adolescents are increasingly interested in weight control; hence, proper health education is important for helping them control their weight properly. This study was designed to pick out social media words that express adolescents’ diet behaviors, and identify the associations and types between such words and the behaviors. It used text-mining techniques and semantic network analysis for related big data collected from the Internet on adolescents’ diet behaviors. Text mining was used to extract meaningful information from unstructured text data, whereas semantic network analysis was used to understand the relationships between keywords. The top five keywords were “obesity,” “health,” “exercise,” “eat,” and “increase” in online news, and “exercise,” “eat,” “weight loss,” “obesity,” and “health” in blogs. The betweenness centrality of “appearance” was particularly higher than that of other centralities in online news. As a result of the CONCOR analysis, eight clusters each were identified in online news and blogs. This study’s results will serve as a basis for weight management-related intervention strategies, reflecting the perspectives of adolescents. It also has significance as basic data to provide correct information, and establish desirable weight control in the future.

Investigating the Effects of the Big Data Analytics on Customer Response in Online Purchases

“…The results also revealed that the recommendation system has more highly significant to the intentions of the individuals than their behavior (Naeem et al, 2022); it means that the recommendation system by companies can boost the intentions of the customers more than their behavior (Le & Liaw, 2017;Müller et al, 2019;Zhang et al, 2022). Information search has also affected customer intention and then after this, it increases the behavior for purchases (Oesterreich et al, 2022;Song et al, 2022). This satisfaction comes because of information provided by the companies through BDA (Akter & Wamba, 2016;Liao & Yang, 2021;Lo & Harvey, 2012).…”

Exploring Korean adolescent stress on social media: a semantic network analysis

“…The data collection and analysis processes used in this study are shown in Fig. 1 and this is the same method as described in the previous study ( Song et al, 2022 ). First, we collected data on adolescent stress by crawling online news and blog websites.…”

“…Yet another study collected data from search engines widely used in Korea to identify social media words that express adolescents’ dietary behaviours and identify the associations and types of such words and behaviours. It used text-mining techniques and SNA for related big data collected from the Internet on adolescents’ dietary behaviours ( Song et al, 2022 ). A study on physical activity and exercise in school-age youth was conducted to provide a solution by analysing a large number of scientific articles through text mining ( Pans et al, 2021 ).…”