• The Journal of Information Systems
• /
• v.27 no.1
• /
• pp.67-88
• /
• 2018

Purpose The purpose of this study is to verify motivations of corporate Facebook usage and to examine the impacts of usage motivations on brand attachment, trust and loyalty. Design/methodology/approach A conceptual model is developed based on Self-determination theory(SDT) and the previous studies. We conducted a web survey with a convenient sample of 121 Facebook users who clicked "Like" button on the corporate Facebook pages. Findings The followings are the findings of the study. First, intrinsic motivation(Entertainment) turned out to have positive effects on brand attachment. Second, extrinsic motivation(information exchange) turned out to have positive effects on brand trust. Third, brand attachment turned out to have positive effects on brand loyalty. These findings provide a valuable basis for constructing an explanatory model for "Like"-clicking behaviors of corporate's Facebook community platform users, as well as making significant practical contributions to enhance social and commercial benefits for businesses and individuals.

• The Journal of Economics, Marketing and Management
• /
• v.11 no.2
• /
• pp.33-46
• /
• 2023

Purpose: The purpose of this study is to analyze the relationship between characteristics of social media sites (SNS) and the intention of Pakistani tourists to visit South Korea while determining the role of usefulness, interestingness, and involvement of tourists. Research design, data and methodology: A research model was developed through the previous research, and the questioner-based survey was conducted on Pakistani tourists visiting Korea. The survey data was collected by following multiple hypotheses: the relationship between SNS tourism information and perception of SNS, the relationship between SNS perception and intention to visit, and adjustment of involvement in the relation between tourism information characteristics, and SNS perception. We used SPSS and AMOS24.0 statistical tools to analyze the hypothesis testing data. Results: Based on the data analysis, the study found that the characteristics of SNS have a positive effect on intention to visit via users' perception like usefulness and interestingness. The involvement has a moderating effect between SNS characteristics and users' perception. In the group with high involvement, the degree of influence of the quality factor of SNS on user perception was greater than in the group with low involvement. Conclusions: This study demonstrated that traveler's involvement has a moderating effect on the relationship between SNS characteristics and visit intention for Pakistani travelers visiting Korea. It shows that practitioners or researchers should establish and operate SNS strategies in consideration of user involvement.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.571-585
• /
• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.34 no.3
• /
• pp.69-76
• /
• 2018

General workers who assist various technicians in different fields with their work across the whole construction sites without having a particular skill are at risk of the highest accident rate and their accident form becomes varied. Accordingly, this study was conducted to identify the relationship between form of safety accident and influence factor in general workers and analyze accident severity by influence factor. The followings are the results from this study. First, as a result of analyzing major form of accident and influence factors in general workers with network analysis methodology, nine forms of accident and seventeen influence factors were drawn. Second, it was found that in accident severity among general workers, collapsing, among various forms of accident, appeared the highest, followed by fall, electric shock, fire, hit by an object, bumped against, trip, scission getting cut chopped in order. Third, main points of special, concentrated, and permanent management were presented in order to reduce the safety accident in general workers effectively.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.61-64
• /
• 2005

The purpose of this paper is to take a closer look at an area having shorted emergence facilities and to determine optional candidate sites instead of vulnerable area by using GIS spatial analysis. Newly determined new candidate is performed by concerning spatial efficiency and spatial equity for a public service. It was determined through using the analyzing of the physical accessibility measure, the Location-Allocation, sort of classic model in spatial statistics and general network analysis. The area of this research has been used in administrative boundary of Young-Dong in Gangneung including 13 emergency, medical hospitals, 46 fire-stations and sub-fire stations. In general terms, what all this show is that the way we are approached for geographical view from using GIS spatial analyzing technique of determined location and allocation problem by the social, economical, political factor and simple administrative discrimination at the meantime. At the same time, with problem occurred in the space it is possible to make an Effective proposal or means, policy, decision for new candidate location-allocation suggesting optimum model.

• Educational Technology International
• /
• v.14 no.2
• /
• pp.189-213
• /
• 2013

The study was conducted to find out the interaction between learning styles, and types of task on satisfaction and achievement in Chinese learning on Facebook. 44 students from D University in Seoul, Korea finished the questionnaires. To measure the participants' learning styles and satisfaction, the learning style instrument and satisfaction instrument were used. The data received were analyzed to find out the interaction between learning styles, and types of task on satisfaction and achievement. Through the analysis, the study suggests that, in the SNS environment for learning, instructors should focus on more on types of tasks than learning styles. Learning styles are important, however, for new pedagogy for one new learning environment, types of task are definitely more important than learning styles. Depending on the study results, the instructors should pay more attention to types of task, and they should also use different strategies to facilitate the contents of tasks to improve achievement and satisfaction in an SNS environment.

• The Journal of the Korea Contents Association
• /
• v.2 no.4
• /
• pp.40-45
• /
• 2002

The network community provides people a new area which is called a cyber area, not physical area. However, today in the cyber area, the negative function has been causing social problem. In fact, it has not been causing by original function. Specifically speaking, main reason why it's leading to the negative function is that teenagers are logging into inappropriate sites (such as: violent, adult sites) to unnecessary sites (such as: e-mails with unknown signs combined with words, inappropriate language used in certain web board). Not only that, private password and ID's are being used by unknown user to accomplish illegal access. If this problem does not get solved through immediate development of technologies method, the cyber crime will increase in short period of time. The purpose of this paper is to understand each analyzed method which can cope with negative internet function, to discuss suitable management in certain web board an to check with fuzzy intercommunication mood.

• Journal of Information Science Theory and Practice
• /
• v.9 no.1
• /
• pp.24-34
• /
• 2021

The popularity of social networking sites (SNS) has facilitated communication between users. The usage of SNS helps users in their daily life in various ways such as sharing of opinions, keeping in touch with old friends, making new friends, and getting information. However, some users misuse SNS to belittle or hurt others using profanities, which is typical in cyberbullying incidents. Thus, in this study, we aim to identify profane words from the ASKfm corpus to analyze the profane word distribution across four different roles involved in cyberbullying based on lexicon dictionary. These four roles are: harasser, victim, bystander that assists the bully, and bystander that defends the victim. Evaluation in this study focused on occurrences of the profane word for each role from the corpus. The top 10 common words used in the corpus are also identified and represented in a graph. Results from the analysis show that these four roles used profane words in their conversation with different weightage and distribution, even though the profane words used are mostly similar. The harasser is the first ranked that used profane words in the conversation compared to other roles. The results can be further explored and considered as a potential feature in a cyberbullying detection model using a machine learning approach. Results in this work will contribute to formulate the suitable representation. It is also useful in modeling a cyberbullying detection model based on the identification of profane word distribution across different cyberbullying roles in social networks for future works.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.294-302
• /
• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.59-66
• /
• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.