Background: Considering that adolescents spend considerable time on the Internet and social media and experience high levels of stress, it is difficult to find a study that investigates adolescent stress through a big data-based network analysis of social media. Hence, this study was designed to provide basic data to establish desirable stress coping strategies for adolescents based on a big data-based network analysis of social media for Korean adolescent stress. The purpose of this study was to (1) identify social media words that express stress in adolescents and (2) investigate the associations between those words and their types. Methods: To analyse adolescent stress, we used social media data collected from online news and blog websites and performed semantic network analysis to understand the relationships among keywords extracted in the collected data. Results: The top five words used by Korean adolescents were counselling, school, suicide, depression, and activity in online news, and diet, exercise, eat, health, and obesity in blogs. As the top keywords of the blog are mainly related to diet and obesity, it reflects adolescents’ high degree of interest in their bodies; the body is also a primary source of adolescent stress. In addition, blogs contained more content about the causes and symptoms of stress than online news, which focused more on stress resolution and coping. This highlights the trend that social blogging is a new channel for sharing personal information. Conclusions: The results of this study are valuable as they were derived through a social big data analysis of data obtained from online news and blogs, providing a wide range of implications related to adolescent stress. Hence this study can contribute basic data for the stress management of adolescents and their mental health management in the future.

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.

This study explores the impact of digital transformation on Korean society by analyzing Korean social media data, focusing on the societal and economic effects triggered by advancements in digital technology. Utilizing text mining techniques and semantic network analysis, we extracted key terms and their relationships from online news and blogs, identifying major themes related to digital transformation. Our analysis, based on data collected from major Korean portals using various related search terms, provides deep insights into how digital evolution influences individuals, businesses, and government sectors. The findings offer a comprehensive view of the technological and social trends emerging from digital transformation, including its policy, economic, and educational implications. This research not only sheds light on the understanding and strategic approaches to digital transformation in Korea but also demonstrates the potential of social media data in analyzing the societal impact of technological advancements, offering valuable resources for future research in effectively navigating the era of digital change.

단위 테스팅은 소프트웨어의 품질과 안정성에 큰 기여를 함에도 불구하고 수작업으로 수행되는 경우가 많다. 일감 관리 도구인 Redmine과 지속 통합 도구 프로그램인 Jenkins는 서버에 설치되어 이용되고 있으므로 구성 및 변경이 어렵다. 본 논문에서는 소프트웨어 프로젝트의 품질을 높이고 테스트의 편의성을 향상시키며, 수동으로 수행하는 테스팅의 불편함을 해소하고자 Python 기반의 단위 테스팅을 자동화하는 클라이언트 프로그램을 Selenium과 REST API를 사용하여 각각 설계하고 구현하였다. 이를 통하여 소프트웨어의 결과물과 개발 프로젝트의 프로세스 및 완성된 프로그램의 품질 향상 및 편의성 증대가 기대된다.

Epistemic Network Analysis (ENA) is a method for quantifying co-occurrence patterns in coded discourse, and the R package rENA is its de facto reference implementation. We present pyena, a Python re-implementation of rENA 0.3.1, validated stage by stage against R outputs. Across adjacency vector construction, sphere-normalized SVD projection, and the means rotation, pyena matches rENA at machine precision (zero at the adjacency stage; below 10−15 at the SVD and means-rotation stages). The package exposes the full pipeline through a scikit-learn-style estimator and adds a single-call group comparison (Welch's t, Mann–Whitney U, permutation) and a reproducibility module quantifying the spread of repeated ENA trajectories.