• Smart Media Journal
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• v.5 no.2
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• pp.8-14
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• 2016

In this paper, we propose a model for coaching and formal operating procedures performed capstone design advisor during the design done to allow effective guidance and management. Coaching model is proposed through coaching and capstone design proposed by coaching on the procedure for the implementation and management, Mokpo National University Department of Computer Engineering student in the program outcomes, performance assessment of the effective operation of the capstone design based social network service the demonstrated effectiveness of the coaching model.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.1-5
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• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.1
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• pp.61-71
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• 2012

Other-regarding preferences (such as trust, reciprocity and altruism) between companies, between consumers and retailers, and between employers and employees are integral elements in determining economic performance. Social trust which is a core element of social capital, especially, is known to reduce transaction costs, help solve collective action problems, and contribute to economic, social, and political development. Therefore, social trust has been given a great deal of attention across an array of academic disciplines for its role in promoting cooperation among individuals and groups, and for its positive influence on economic performance. Most studies describe Korea as a low-trust society than Japan or China. To identify the causes of social trust, this paper focuses on differences of social values (perception on competition and wealth accumulation) in 4 countries (Japan, China, Korea, and United States). Based on World Values Survey data, this paper analyzes effects of the social values on social trust. Social trust was measured by degree to which a respondent thinks that most people can be trusted. Perception on competition was measured by the degree to which a respondent thinks that competition is harmful, and perception on wealth accumulation was done by the degree to which a respondent thinks that wealth can grow so there is enough for everyone. The results showed that social trust was affected by perception on competition and wealth accumulation. A respondent showed higher level of social trust when he (or she) perceived positively competition and wealth accumulation. For enhancing social trust in a country, it is not easy to reduce income inequality and corruption which were reported as causes of social trust by previous studies. Compared to them, social values can be changed more easily by various concrete measures like education and mass-media. Differently from previous studies this paper stresses the concrete measures to enhance social trust in a country.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• Korean Journal of Culture and Arts Education Studies
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• v.6 no.3
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• pp.21-37
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• 2011

The purpose of this study was to discuss the cultural competency model which can be applied for all the fields in one's life and to develop the cultural competency of media part for school education, lifelong education, and corporate education. To achieve this purpose, the cultural competency model of media part was developed through literature review, panel workshop with high-performers of media part, and experts verification. In the result of this study, core performance of media part was identified. They were improvement of work efficiency, possibility of creative design, change of media context, increase of understanding and participation to social culture, improvement of relationship skill, increase of chance of self-examination, and self-satisfaction. It was 62 that The cultural competencies which were needed to achieve these core performance, then, finally the definition and standard of behaviors of each competencies were drawn and the cultural competency model was proposed.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• The Research Journal of the Costume Culture
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• v.28 no.4
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• pp.562-571
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• 2020

Intermedia refers to the convergence of media. The advance of intermedia has not only facilitated the delivery of brand messages in contemporary fashion shows but also facilitated interactive communication. This study investigated the mediating roles played by various media in fashion and fashion shows, focusing on the phenomenon of intermedia in contemporary fashion shows. To investigate the impact of intermedia on contemporary fashion shows, we conducted a social network analysis-a promising approach for research into fashion trends. Analyzing 159 fashion-related articles published in the 2000s, we extracted intermedia-related words (n=253). The relation-ships between keywords made an analysis of between centrality, and cluster variables applied Clauset-Newman-Moore by using KrKwic and NodeXL programs. The results of the between centrality analysis indicated that the most important factors in contemporary fashion shows are "models" and "stages." We found that the impacts of intermedia on contemporary fashion shows can be divided into four categories: "model performance," "symbolic stage management," "new media utilization," and "convergence in arts." Our analysis thus identified considerable synergy between the characteristics of intermedia and contemporary fashion shows. These results have found intermedia-related commonalities in intermedia and fashion show, and this might increase customer interest in fashion, a positive outcome for the fashion industry.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.479-491
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• 2008

A "task network" is a type of social network that consists of experts who exchange professional help and advice regarding executing tasks. In this study, we investigate the task network used within the IS department of a national bank in Korea. We identify how this network moderates the influence of computer-mediated communication (CMC) technology on an individual s task performance. Size, density, and centrality were measured as the characteristics of a personal task networks. Size equates to the total number of colleagues who work with a specific member for a certain project. Density is the ratio of the number of actual relationships to the total number of available relationships. Centrality defines whether an individual s position is in the exact center of whole network, and is measured by betweenness centrality, meaning the position one member holds between others in a network. Our findings conclude that the conditions - the larger the size of the task network, the smaller its density and the higher its level of centrality - lead to more benefits of using CMC media. Further, this positive effect of CMC is more noticeable when it provides synchronicity.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.269-286
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• 2013

The advent of Web 2.0 and social media is taking a leading role of emerging big data. At the same time, however, informational dysfunction such as infringement of one's rights and violation of social order has been increasing sharply. This study, therefore, aims at defining malicious usage, identifying malicious feature, and devising an automated method for classifying them. In particular, the rule-based experiment reveals statistically significant performance enhancement.