• Asia pacific journal of information systems
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• 제25권2호
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• pp.289-305
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• 2015

The dramatic growth of social networking sites (SNS) has created a myriad of privacy concerns. Our study focuses on how much monetary incentive SNS users are willing to accept in exchange for disclosing their SNS information by accepting friend requests. First, we focused on information privacy in SNS, and estimated the value of information privacy by using the contingent valuation method. Second, we attempted to estimate how SNS users' willingness to accept would change when demographic information and additional information vary. Privacy-sensitive SNS users have the following characteristics: higher education, less SNS experience, and higher security consciousness. On the contrary, those who make good use of SNS and use open-based SNS are less sensitive to privacy. In summary, privacy-sensitive SNS users are fearful or uneasy when they have insufficient control of SNS information. Considering 14 conditions on the value of information privacy on SNS, the mean value of SNS information per person is 173,957 won. If we apply this value to Facebook users, the total Facebook information value would be 1.91 trillion won, considering that there are 11 million users in Korea.

• 한국정보시스템학회지:정보시스템연구
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• 제27권1호
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• pp.67-88
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• 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.

• 융합경영연구
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• 제11권2호
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• pp.33-46
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• 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.

• 대한원격탐사학회지
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• 제38권5_1호
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• pp.571-585
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• 2022

기후변화로 인한 가뭄, 홍수, 산불, 산사태 등 자연재해의 위협이 증가함에 따라, 합성개구레이더(Synthetic Aperture Radar, SAR)와 같이 고해상도 토양수분 복원에 대한 사회적 수요도 증가하고 있다. 하지만 국내 환경은 산림 지형의 비율이 높아, 식생과 지형의 영향을 크게 받는 SAR 자료에서 토양수분을 복원하는데 많은 어려움을 겪고 있다. 이에 본 연구에서는 기계학습의 일종인 인공신경망(Artificial Neural Network, ANN) 기법을 활용하여, Sentinel-1 SAR 영상의 자립형 활용성을 평가하였다. Sentinel-1에서 얻을 수 있는 이중편파 산란계수는 토양수분 거동과 유의미한 상관성을 가지고 있음을 확인할 수 있었으며, 다른 위성이나 지점에서 관측된 보조자료를 사용하지 않고도 식생의 효과 등을 보정할 수 있는 자립형 활용 가능성도 확인할 수 있었다. 하지만 각 지점별, 지형 그룹별 특성에 의한 차이가 크게 나타났으며, 특히 산지와 평지에서 학습된 모형을 교차적용하였을 때 토양수분을 제대로 모의할 수 없는 현상이 발생하였다. 또한 이러한 문제를 해결하고자 학습 지점의 수를 늘리는 경우에는 토양수분 복원 모형이 평활화되어 상관계수는 증가하였으나, 지점에서의 오차는 점점 증가하였다. 따라서 고해상도 SAR 토양수분 자료를 광범위하게 적용하기 위해서는 체계적 연구 수행이 선행되어야 하며, 목적에 따른 학습 지점의 선정, 적용 지역의 범위 등을 구체적으로 제한하여 활용한다면 다양한 분야에서 효과적으로 활용할 수 있을 것으로 기대된다.

• 대한건축학회논문집:구조계
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• 제34권3호
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• pp.69-76
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• 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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.61-64
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• 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
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• 제14권2호
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• pp.189-213
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• 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.

• 한국콘텐츠학회논문지
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• 제2권4호
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• pp.40-45
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• 2002

인터넷에 의해 도래된 네트웍 사회는 물리적 공간이 아닌 사이버 공간이라는 새로운 공간을 인간에게 제공하였다. 그러나 현재는 사이버 공간에서 순기능이 아닌 역기능이 사회적 문제로 대두되고 있다. 구체적으로 말해 청소년 등이 음란 외설이나 폭력 같은 불건전한 정보의 무분별한 접속에 따른 윤리 교육에서부터 시작해서 국적 불명의 단어와 기호 등을 조합하여 사용하는 e-mail 그리고 인터넷 자유게시판에서 상대방에 대한 성적 모독, 욕설, 상대방 비하, 근거 없는 비방 등과 같은 인터넷 역기능에 대한 문제가 이슈가 되고 있다. 그 뿐 아니라, ID나 패스워드 등을 도용한 불법 엑세스도 만연하여 이를 해결키 위한 기술적 방법이 강구되지 않으면 사이버 범죄에 따른 피해가 대단히 클 것으로 여겨진다. 본 논문에서는 이 같은 인터넷 역기능에 대처키 위한 동향 등을 살펴보고 특히 인터넷 자유게시판에서의 적절한 운영 방안 등에 대해 논하고자 하며 이를 퍼지 의사 모델에 의해 검토하기로 한다.

• Journal of Information Science Theory and Practice
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• 제9권1호
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• pp.24-34
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• 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
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• 제22권5호
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• pp.294-302
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• 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%.