Depression in South Korean Adolescents Captured by Text and Opinion Mining of Social Big Data.

Depression in adolescence is recognized as an important social and public health issue that interferes with continued physical growth and increases the likelihood of other mental disorders. The goal of this study was to examine online documents posted by South Korean adolescents for 3 years through the text and opinion mining of collectable documents in order to capture their depression. The sample for this study was online text-based individual documents that contained depression-related words among adolescents, and these were collected from 215 social media websites in South Korea from 1 January 2012 to 31 December 2014.

Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data.

COVID-19 is a respiratory infectious disease that first reported in Wuhan, China, in December 2019. With COVID-19 spreading to patients worldwide, the WHO declared it a pandemic on 11 March 2020. This study collected 1,746,347 tweets from the Korean-language version of Twitter between February and May 2020 to explore future signals of COVID-19 and present response strategies for information diffusion.

Correction: A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology.

[This corrects the article DOI: 10.2196/25028.].

A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology.

Background: South Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends.

Objective: The aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues.

Using Social Media Data to Understand Consumers' Information Needs and Emotions Regarding Cancer: Ontology-Based Data Analysis Study.

Background: Analysis of posts on social media is effective in investigating health information needs for disease management and identifying people's emotional status related to disease. An ontology is needed for semantic analysis of social media data.

Objective: This study was performed to develop a cancer ontology with terminology containing consumer terms and to analyze social media data to identify health information needs and emotions related to cancer.

Social Big-Data Analysis of Particulate Matter, Health, and Society.

The study collected particulate matter (PM)-related documents in Korea and classified main keywords related to particulate matter, health, and social problems using text and opinion mining. The study attempted to present a prediction model for important causes related to particulate matter by using social big-data analysis. Topics related to particulate matter were collected from online (online news sites, blogs, cafés, social network services, and bulletin boards) from 1 January 2015, to 31 May 2016, and 226,977 text documents were included in the analysis.

Application of Social Big Data to Identify Trends of School Bullying Forms in South Korea.

As the contemporary phenomenon of school bullying has become more widespread, diverse, and frequent among adolescents in Korea, social big data may offer a new methodological paradigm for understanding the trends of school bullying in the digital era. This study identified Term Frequency-Inverse Document Frequency (TF-IDF) and Future Signals of 177 school bullying forms to understand the current and future bullying experiences of adolescents from 436,508 web documents collected between 1 January 2013, and 31 December 2017. In social big data, sexual bullying rapidly increased, and physical and cyber bullying had high frequency with a high rate of growth.

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology.

Background: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates.

Objective: The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data.

Correction: Development and Evaluation of an Obesity Ontology for Social Big Data Analysis.

[This corrects the article on p. 159 in vol. 23, PMID: 28875050.

Longitudinal Trajectory of the Relationship Between Self-Esteem and Substance Use From Adolescence to Young Adulthood.

Background: We examined the longitudinal trajectory of substance use (binge drinking, marijuana use, and cocaine use) in relation to self-esteem from adolescence to young adulthood.

Methods: Generalized estimating equation models were fit using SAS to investigate changes in the relation between self-esteem and each substance use (binge drinking, marijuana use, and cocaine use) from adolescence to young adulthood. Data were drawn from the 3 waves of the National Longitudinal Study of Adolescent Health, a nationally representative sample of middle and high school students in the United States (N = 6504).

Analysis of the Factors Affecting Consumer Acceptance of Accredited Online Health Information.

With the increasing use of the internet and the spread of smartphones, health information seekers obtain considerable information through the internet. As the amount of online health information increases, the need for quality management of health information has been emphasized. The purpose of this study was to investigate the factors affecting the intention of using accredited online health information by applying the extended technology acceptance model (Extended-TAM).

Development and Evaluation of an Obesity Ontology for Social Big Data Analysis.

Objectives: The aim of this study was to develop and evaluate an obesity ontology as a framework for collecting and analyzing unstructured obesity-related social media posts.

Methods: The obesity ontology was developed according to the 'Ontology Development 101'. The coverage rate of the developed ontology was examined by mapping concepts and terms of the ontology with concepts and terms extracted from obesity-related Twitter postings.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

Background: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics.

Objective: The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis.

Social Big Data Analysis of Information Spread and Perceived Infection Risk During the 2015 Middle East Respiratory Syndrome Outbreak in South Korea.

We investigated online diffusion of information, spread of fear, and perceived risk of infection to Middle East Respiratory Syndrome (MERS) as cases of MERS spread rapidly and dozens of fatalities occurred in South Korea in May-June of 2015. This study retrieved 8,671,695 MERS-related online documents from May 20 to June 18, 2015, from 171 Korean online channels and analyzed such documents by using multilevel models and data mining with Apriori algorithm association analysis. We used R software (version 3.

Development and Evaluation of a Low Fertility Ontology for Analyzing Social Data in Korea.

The purpose of this study is to develop a low fertility ontology for collecting and analyzing social data. A low fertility ontology was developed according to Ontology Development 101 and formally represented using Protégé. The content coverage of the ontology was evaluated using 1,387 narratives posted by the public and 63 narratives posted by public servants.

Development and Evaluation of an Adolescents' Depression Ontology for Analyzing Social Data.

This study aims to develop and evaluate an ontology for adolescents' depression to be used for collecting and analyzing social data. The ontology was developed according to the 'ontology development 101' methodology. Concepts were extracted from clinical practice guidelines and related literatures.

Big data analysis framework for healthcare and social sectors in Korea.

Objectives: We reviewed applications of big data analysis of healthcare and social services in developed countries, and subsequently devised a framework for such an analysis in Korea.

Methods: We reviewed the status of implementing big data analysis of health care and social services in developed countries, and strategies used by the Ministry of Health and Welfare of Korea (Government 3.0).

Psychometric evaluation of a patient safety competency self-evaluation tool for nursing students.

This study aimed to evaluate the reliability and validity of a patient safety competency self-evaluation (PSCSE) tool. An exploratory factor analysis (EFA) was used to investigate the compositions of the PSCSE. The internal structure of the PSCSE was schematized using a confirmatory factor analysis (CFA).

Stress, Depression, and Lifestyle Behaviors in Korean Adults: A Latent Means and Multi-Group Analysis on the Korea Health Panel Data.

It has been reported that stress can induce depression, with the patient's age and sex as moderating factors. Associations between depression and lifestyle in Korean adults have not been addressed. This study was designed to examine if the relationships among stress, problem drinking, exercise, and depression differ by age and sex.

Development of health information search engine based on metadata and ontology.

Objectives: The aim of the study was to develop a metadata and ontology-based health information search engine ensuring semantic interoperability to collect and provide health information using different application programs.

Methods: Health information metadata ontology was developed using a distributed semantic Web content publishing model based on vocabularies used to index the contents generated by the information producers as well as those used to search the contents by the users. Vocabulary for health information ontology was mapped to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and a list of about 1,500 terms was proposed.

A three-year autoregressive cross-lagged panel analysis on nicotine dependence and average smoking.

Objectives: Previous studies have been limited to the use of cross sectional data to identify the relationships between nicotine dependence and smoking. Therefore, it is difficult to determine a causal direction between the two variables. The purposes of this study were to 1) test whether nicotine dependence or average smoking was a more influential factor in smoking cessation; and 2) propose effective ways to quit smoking as determined by the causal relations identified.