• Clinical and Experimental Pediatrics
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• v.56 no.1
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• pp.1-7
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• 2013

School bullying has become a major social problem in Korea after the emergence of media reports on children who committed suicide after being victimized by bullies. In this article, we review the characteristics of bullying, and investigate the role of the pediatrician in the prevention of and intervention against bullying and school violence. Bullying can take on many forms such as physical threat, verbal humiliation, malicious rumors, and social ostracism. The prevalence of bullying in various countries is approximately 10% to 20%. In Korea, the prevalence of school violence is similar but seems to be more intense because of the highly competitive environment. From our review of literature, we found that children who were bullied had a significantly higher risk of developing psychosomatic and psychosocial problems such as headache, abdominal pain, anxiety, and depression than those who were not bullied. Hence, it is important for health practitioners to detect these signs in a child who was bullied by questioning and examining the child, and to determine whether bullying plays a contributing role when a child exhibits such signs. Pediatricians can play an important role in the prevention of or intervention against school violence along with school authorities, parents, and community leaders. Moreover, guidelines to prevent school violence, such as the Olweus Bullying Prevention Program, KiVa of the Finish Ministry of Education, and Connected Kids: Safe, Strong, Secure of the American Academy Pediatrics, should be implemented.

• Asian Journal for Public Opinion Research
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• v.8 no.3
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• pp.246-264
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• 2020

As netizens increasingly utilize social media to obtain and engage with information, this study aims to determine the extent to which the follower-influencer interaction is manifested and strengthened. To analyze information related to the novel coronavirus disease (COVID-19), a total of 62,119 online posts from 11 Internet forums were examined to find a relationship between followers and influencers in Taiwan. These forums are PTT, SOGO, Ck101, Plurk, Mobile01, TalkFetnet, Gamez, PlaySport, Dcard, Eyny, and PCDVD. The variables that were the best predictors of influencer classification were strong influences, engagements, and hot values across 11 Internet forums. Learning the response to the COVID-19 pandemic is vital because public actions could have been fueled by stigmatizing terms that may harm public health and well-being. The results questioned the conventional diffusion of traditional news sources because the influencers brought widespread attention to the health threat issues in the early outbreak stages. This study enhances the understanding of forum types, follower engagement, and influencers' impact maximization in social networks. The conclusion provides insight into the relationships and information diffusion mechanisms to ensure accurate health information dissemination.

• The Journal of Information Systems
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• v.25 no.3
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• pp.1-30
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• 2016

Purpose The purpose of this study is to grasp the factors influencing domestic SNS users' privacy protection behavior and verify their relationship through self-efficacy and responsiveness. Thus, this study tries to suggest efficient and effective measures for SNS personal information protection. Design/methodology/approach To this end, with main variables of the protection motivation theory based on the assumption that when users are exposed to the threat to their health, they would have protection motivation and change their behavior of protecting their health, a research model was suggested. In addition, in order to empirically verify the research model, a survey was performed targeting general college students having the experience of using SNS. Findings As a result of the analysis, first, perceived effectiveness and self-efficacy had a positive effect on responsiveness. Second, perceived barrier had a positive effect on self-efficacy. Third, self-efficacy and responsiveness had a positive effect on privacy protection behavior. This study is expected to contribute to establishing an effective guideline for measures that could induce SNS users' privacy protection behavior.

• The Journal of Asian Finance, Economics and Business
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• v.4 no.4
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• pp.5-15
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• 2017

The Northern States of Malaysia comprises of four states (Penang, Kedah, Perlis and Perak) still record high poverty incidence eventhough Malaysia has experienced a remarkable reduction of poverty over the past century. Economic activities in Perlis and Kedah that are predominantly agriculture in the rural area contribute to this disparity. To add, rural households are also subject to risks and uncertainties that make them more vulnerable to poverty. This study examines the impact of risks and assets on households' vulnerability to poverty. A survey of 400 respondents was conducted in December 2015 in the northern region of Malaysia. From these 400 questionnaires, only 298 were considered valid and used in the analysis. Using a logistic probability function, the results indicated that risks are not a significant threat to households. Gender and strata are crucial elements that significantly determine households' vulnerability. While human capital and financial capital significantly reduce households' vulnerability to poverty, physical and natural capitals were not statistically significant. The study suggests that the government and practitioners design strategies and policies with an assets-based approach. The asset-based approach is more appropriate for linking the causes of poverty to vulnerability.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.183-191
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• 2012

Since June 25th 1950, the beginning of the cold war (Korean war), Korean peninsula is still in a state of war. Officially South and North Korean government call a truceafter three years from the beginning day, however both countries are still having several combats in these days. So every Korean citizen male has duty for serving military duty and this lasts even after the serving regular military force, as reserved military. Although South Korea is very small country, the size of military is very large so informing all reserved military takes some time. Since this nation is confronting the enemy and considering the global potential threat, South Korean military needs expedite informing system to call up the reserved military to active duty. In this project, the current informing system has been analyzed and compared with the new method which is using social network service such as Twitter. However mobilization order is very critical. So in our new model there are two ways combined. Using twitter to inform and then use traditional ways to finish the order. This method will provide more efficient and accurate way to cover the call ups.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1014-1028
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• 2017

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

• Journal of The Geomorphological Association of Korea
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• v.24 no.4
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• pp.75-89
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• 2017

Sinkholes are key karst landforms that primarily evolve through the dissolution of limestone, and it posing a significant threat to roads, buildings, and other man-made structures. This study aims to analyze the area susceptible to sinkhole development using GIS and to identify potential danger area from sinkholes. Eight sinkhole related factors (slope angle, distance to caves, distance to faults, bedrock lithology, soil depth, drainage class, distance to mines, and distance to traffic routes) were constructed as spatial databases with sinkhole inventory. Based on the spatial database, sinkhole susceptibility maps were produced using nearest neighbor distance and frequency ratio models. The maps were verified with prediction rate curve and area under curve. The result indicates that the nearest neighbor distance and frequency ratio models predicted 95.3% and 94.4% of possible sinkhole locations respectively. Furthermore, to identify potential sinkhole danger area, the susceptibility map was compared with population distribution and land use map. It has been found that very highly susceptible areas are along Osipcheon and southeast southwest part of Hajang-myeon and south part of Gagok-myeon of Samcheok-si. Among those areas, it has been identified that potential sinkhole danger areas are Gyo-dong, Seongnae-dong, Jeongna-dong, Namyang-dong and Dogye-eup. These results can be useful in the aspects of land use planning and hazard prevention and management.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• Journal of Information Science Theory and Practice
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• v.7 no.1
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• pp.17-28
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• 2019

Dengue infection is becoming a serious global health threat. Public awareness is a pre-requisite for the successful implementation of dengue prevention programs. The main purpose of this study was to investigate dengue-related information needs and seeking behavior of the general public in Singapore. Some areas covered by this study were: importance of dengue-related information needs, preferred channels for seeking information, and respondents' perceptions of using dengue-related information. A questionnaire was used for data collection and 152 individuals participated in this study. Data analysis showed that the most sought after information concerned: dengue-related medicines, primary symptoms of dengue infection, and different possible treatments. The popular channels for seeking information were: websites of hospitals and other health agencies, the social media, television, and newspapers. Medical staff, such as doctors and nurses, were trusted for providing accurate information. Although credibility of social media was considered low, respondents were using it due to its easy accessibility. The findings of this study will be useful to government health departments in Singapore as well as in other countries suffering from dengue, hospitals, and public welfare agencies involved in public health awareness campaigns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.