• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.77-84
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• 2024

Emotional health is important for overall health, and those who are experiencing difficulties should seek professional help. However, the social stigma associated with emotional health, as well as the influence of cultural beliefs, prevent many people from seeking help. This makes early detection difficult, which is critical for such health issues. It would be extremely beneficial if they could assess their emotional state and express their thoughts without prejudices. On the market, there are emotional health apps. However, there was little to no evidence-based information on their quality. Hence, this study was conducted in order to provide evidence-based quality in emotional health mobile apps. Eleven functionality task scenarios were used to assess functional quality, while a System Usability Scale test (n=20) was used to assess usability, customer acceptability, learnability, and satisfaction. The findings show that the app for emotional health management is highly efficient and effective, with a high level of user satisfaction. This contributes to the creation of an app that will be useful and practical for people experiencing early-stage emotional health issues, as well as related stakeholders, in order to manage early-stage emotional health issues.

• The Journal of Korean society of community based occupational therapy
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• v.3 no.1
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• pp.11-20
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• 2013

Objective : The purpose of this study is to identify the impact of the social support and social network on the quality of life of the elderly residing in a local community. Method : The subjects of this study were 75 healthy old men and women of 13 sites of welfare centers for the disabled and public health centers and senior welfare centers in Busan and Gyeongju. A survey was conducted with a questionnaire that include general characteristics, cognitive ability, social support, social network and quality of life. The analysis was made on 63 replies except 12 subjects who had been excluded by the subject selection criteria. Result : As a result of analyzing correlation of variables affecting life quality, there was positive correlation in contact frequency(p<.05), intimacy(p<.001), and social support(p<.001). Finally, it was analyzed that the variable of intimacy (p<.001) affected life quality of general aged people living in regional community. Conclusion : It was found that intimacy of general aged people living in regional community was a major variable to affect life quality. It could be identified that intimacy which is qualitative feature of social, relational network for the aged who live passive life was important.

• Management & Information Systems Review
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• v.35 no.2
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• pp.221-254
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• 2016

With increasing interest of health due to population aging, medical device industry is highlighted as a promising industry. However, Korea medical device industry is not enough market competitiveness compared to global company due to a narrow domestic market and a small company structure. In order to retain the national competitiveness, it is necessary that we have to derive a central technology and its trend. This study has predicted a central technology for medical device industrial using patent network analysis. The central technology is defined as a key technology that is connected to most other technologies and that significantly affects them. For the empirical study, we conducted social network analysis using covariance and correlation coefficient between IPC codes extracted from medical device patents, introduced by Jun(2012). A social network is a social structure of diverse items as well as of human beings. In this study, we set each medical device as a node in an SNA and analyze the Degree values between them. Also, Korea health industrial statistics system are utilized for verification of selected central technology. As a result, we found that the central technology is located on the medical device items, which are listed higher the amount of production. The central technology selected through the proposed methodology will provide a inspiration for establishment of R&D policy.

• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.342-350
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• 2022

Objectives: Human immunodeficiency virus (HIV) prevention among youth seems under-prioritised compared to other key populations. HIV knowledge and stigma are important parts of HIV prevention. To inform HIV prevention among youths, this study quantitatively analysed the associations between open communication regarding sexuality and sexual health, comprehensive HIV knowledge, and non-stigmatising attitudes in Indonesia. Methods: This study used data from the Indonesian Demographic and Health Survey (IDHS) 2017. The analysis included unmarried men and women aged 15-25 years old. Comprehensive HIV knowledge and a stigmatising attitude were defined according to the IDHS 2017. Open communication about sexuality and sexual health was defined as the number of people with whom participants could openly discuss these topics in their direct network of friends, family, and service providers, with a scale ranging from 0 to a maximum of 7. Primary analysis used binomial logistic regression with weighting adjustments. Results: The final analysis included 22 864 respondents. Twenty-two percent of youth had no one in their direct network with whom to openly discuss sexual matters, only 14.1% had comprehensive HIV knowledge, and 85.9% showed stigmatising attitudes. Youth mostly discussed sex with their friends (55.2%), and were less likely to discuss it with family members, showing a predominant pattern of peer-to-peer communication. Multivariate analysis showed that having a larger network for communication about sexuality and sexual health was associated with more HIV knowledge and less stigmatising attitudes. Conclusions: Having more opportunities for open sex communication in one's direct social network is associated with more HIV knowledge and less stigmatising attitudes.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.8
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• pp.3635-3640
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• 2014

Aim: To determine whether induced abortion (IA) increases breast cancer (BC) risk. Materials and Methods: A population-based case-control study was performed from Dec, 2000 to November, 2004 in Shanghai, China, where IA could be verified through the family planning network and client medical records. Structured questionnaires were completed by 1,517 cases with primary invasive epithelial breast cancer and 1,573 controls frequency-matched to cases for age group. The information was supplemented and verified by the family planning records. Statistical analysis was conducted with SAS 9.0. Results: After adjusting for potential confounders, induced abortions were not found to be associated with breast cancer with OR=0.94 (95%CI= 0.79-1.11). Compared to parous women without induced abortion, parous women with 3 or more times induced abortion (OR=0.66, 95%CI=0.46 to 0.95) and women with 3 or more times induced abortion after the first live birth (OR=0.66, 95%CI =0.45 to 0.97) showed a lower risk of breast cancer, after adjustment for age, level of education, annual income per capita, age at menarche, menopause, parity times, spontaneous abortion, age at first live birth, breast-feeding, oral contraceptives, hormones drug, breast disease, BMI, drinking alcohol, drinking tea, taking vitamin/calcium tablet, physical activity, vocation, history of breast cancer, eating the bean. Conclusions: The results suggest that a history of induced abortions may not increase the risk of breast cancer.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Women's Health Nursing
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• v.1 no.1
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• pp.28-43
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• 1995

The relationship between perceived social support and adaptation to maternal role for first-time mothers was investigated in this descriptive correlational study. A nonprobable sample of 90 first-time mothers were selected, who had uncomplicated perinatal experiences and delivered healthy and term newborns as well. The data was collected during a home interview at 4-6weeks postpartum. The outcome of adaptations was defined as the level of sensitivity in parent-infant interactions and of the self confidence in infant care. The perception of social support in the primiparous was assessed by the NSSQ during the postpartum. The results obtained from this study are summarized as follows : 1. The mean score of the perceived total functional support was $116.6{\pm}37.5$ points (affective : 38.1 affirmative : 39.3, aid : 39.3), and the score of the total network support was $45.2{\pm}13.9$ points (size : 4.9, duration :19.8 frequency : 20.4). These scores tended to be slightly low. 2. The mean score of the self confidence on the infant care activity as the subjective aspect of the maternal role adaptation (MRA) was 56.5 points (86.9%), whereas that of the sensitivity of the mother-infant interaction of the MRA was 78.9 points (63.2%). 3. The subjective aspect of the MRA has showed a positive relation ship with the aid dimension of the functional support. And the objective aspect of the MRA also showed a positive relationship with the total functional support and the total network support. However the correlating degrees were slightly low. In conclusion, the primiparous mothers perceived that they had received a small amount of social support during the postpartum period, suggesting the need of various kinds of social support to promote the MRA for the primiparous.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.