• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1868-1887
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• 2024

Recommendation systems research is a subfield of information retrieval, as these systems recommend appropriate items to users during their visits. Appropriate recommendation results will help users save time searching while increasing productivity at work, travel, or shopping. The problem becomes more difficult when the items are geographical locations on the ground, as they are associated with a wealth of contextual information, such as geographical location, opening time, and sequence of related locations. Furthermore, on social networking platforms that allow users to check in or express interest when visiting a specific location, their friends receive this signal by spreading the word on that online social network. Consideration should be given to relationship data extracted from online social networking platforms, as well as their impact on the geolocation recommendation process. In this study, we compare the similarity of geographic locations based on their distance on the ground and their correlation with users who have checked in at those locations. When calculating feature embeddings for users and locations, social relationships are also considered as attention signals. The similarity value between location and correlation between users will be exploited in the overall architecture of the recommendation model, which will employ graph convolution networks to generate recommendations with high precision and recall. The proposed model is implemented and executed on popular datasets, then compared to baseline models to assess its overall effectiveness.

• 한국콘텐츠학회논문지
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• 제15권4호
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• pp.391-406
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• 2015

온라인 뉴스에서 개인의 참여가 활성화 되면서 댓글의 중요성이 부각되고 있다. 최근엔 개인의 SNS(social networking site) 계정을 이용하여 댓글을 게재할 수 있는 소셜댓글 서비스가 활성화 되고 있다. 본 연구에서는 실제 온라인 뉴스 댓글 현황 데이터를 이용하여 (1) 댓글의 일반적 특성요소 중 일반댓글과 소셜댓글이 차이점을 보일 가능성이 있는 요소를 도출한 후, (2) 일반댓글에 비해 소셜댓글이 각 특성요소별로 어떻게 다른지 비교 분석하고, 마지막으로 (3) 소셜댓글 이용 업체별로 각 특성요소가 어떻게 달라지는지를 실증 분석해보았다. 이를 위해 기존문헌 조사 및 전문가 인터뷰를 진행하여 여섯 가지 특성요소를 도출하였다. 다음으로 SPSS Statistics의 t-test의 분석 방법을 사용하여, 소셜댓글과 일반댓글이 모든 요소에서 유의한 차이를 보임을 확인하였고, ANOVA와 Duncan test 결과 트위터와 페이스북 그룹 간 차이가 유의함을 확인하였다. 본 연구를 통해 소셜댓글의 실제적인 가치를 명확히 파악할 수 있을 뿐만 아니라, 소셜댓글을 이용한 악성댓글 문제 해결에 실마리를 제공하고, 개인, 기업, 정부기관 등을 주체로 다른 분야의 적용가능성도 살펴볼 수 있을 것으로 기대한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 추계학술발표대회
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• pp.196-198
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• 2016

Online social networking sites such as MySpace, Facebook, Twitter are becoming very preeminent, and the quantities of their users are escalating very quickly. Due to the significant escalation of security vulnerabilities in social networks, user's confidentiality, authenticity, and privacy have been affected too. In this paper, a short study of online social network attacks is presented in order to identify the problems and impact of the attacks on World Wide Web (WWW).

• Agribusiness and Information Management
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• 제1권2호
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• pp.81-94
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• 2009

Recently, social network service is developing rapidly as technology changes with new mobile dimensions and features creating positive opportunities and benefits to all users and companies. Social network services are allowing companies to expand their businesses and brands by utilizing it as a marketing tool to reach customers. This research is intended to identify major online social network services and their trends while enhancing the understanding of food service business expansion through social networking.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.613-624
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• 2021

The use of social networking sites (SNS) for e-recruitment has shifted the focus away from traditional hiring and selection processes. They are commonly used in the search and acquisition of new employees and are projected to expand in the near future as an e-recruitment tool. However, there is a lack of material on SNS and their impact on an employers' intention to use these sites for e-recruitment, in the context of Malaysia. Hence, there is an acute necessity for research on the extent that the features of SNS can influence the employers' intention to use SNS for e-recruitment and to know how to keep utilizing the platform for future e-recruitment. This study aims to identify the key features of SNS that lead to employers' intention to use SNS for e-recruitment in Malaysia. In this cross-sectional study, random sampling was utilized to obtain data from 198 recruitment professionals using online survey. The findings show that data quality, reliability, networking spectrum and simplicity of navigation of SNS are the key predicting factors for intention to use SNS for e-recruitment. Therefore, employers should acknowledge these key features of SNS to achieve their e-recruitment goals.

• 정보교육학회논문지
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• 제22권1호
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• pp.159-167
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• 2018

본 연구에서는 오픈소스 소셜네트워크 소프트웨어인 Humhub를 사용하여 온라인 학습 커뮤니티 사이트를 구축하고, 온라인 사이트에서 그룹을 만들어 그룹내에서 질의 응답을 통한 지식의 사회적 구성이 가능하도록 하였다. 학습 커뮤니티 사이트에 질의 응답에 대한 학습 자료들이 축적되게 함으로써 학습자들이 언제든지 찾아서 학습할 수 있고, 자기 주도적인 학습 공동체를 만들어 지식을 소비하는 것 뿐만 아니라 지식을 재구성 할 수 있는 기회를 제공하였다. 또한 수식 입력이 가능하도록 수식 입력 기능을 개발하여 학습자들이 온라인으로 수식을 사용할 수 있도록 하였다. 온라인 학습 커뮤니티 사이트는 탐구 기반 정보 교육에 활용될 수 있다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.957-959
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• 2013

SNS(Social Networking Service)는 사용자 간의 자유로운 의사소통과 정보 공유, 그리고 인맥 확대 등을 통해 사회적 관계를 생성하고 강화시켜주는 온라인 플랫폼을 의미한다. 이 연구에서는 SNS에서 주요 개체들을 발견하고 그들간의 관계를 도출하고, 이들을 기반으로 ERD를 그린다. 작성한 ERD를 NoSQL 데이터베이스인 MongoDB 데이터 모델의 컬렉션들로 변환함으로써, SNS 데이터베이스의 주요 스키마를 설계한다.

• The Journal of Asian Finance, Economics and Business
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• 제5권4호
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• pp.107-116
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• 2018

The aim of this study is to delve deeper into the online brand community study. This study tests (a) the effects of online brand community on its outcomes, (b) the impact of value creation practice construct as a whole, (c) the effects of value creating practice construct on the two types of loyalty, loyalty toward the brand and the community. Participants of this study (N=353) are members of four types of online brand communities (e.g., business-to-consumer virtual product support community, firm-hosted online community, user-generated online community, peer-to-peer problem-solving community, and social media based brand community). Data were collected online using Amazon Mechanical Turk from April 10, 2016 to May 10, 2016. The data were analyzed through structural equations modeling using AMOS 20. The three community markers (e.g., consciousness of kind, rituals and traditions, and moral responsibility) and the four value creation practices (e.g., social networking, impression management, community engagement, and brand use) are proved to be significant indicators of online brand community and value creation practice constructs, respectively. Test results showed that strong and effective online brand communities generate value creation practices, and value creation practices enhance brand loyalty. The mediating effects of community loyalty between value creation practices and brand loyalty were revealed.

• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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• pp.69-94
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• 2023

Purpose Social networking service (SNS) platforms employ distinct networking strategies to meet the varying needs of their users, resulting in divergent sets of technological functionalities offered by each platform. Consequently, unique features on various SNSs give rise to distinct social issues. Moreover, the available technical coping mechanisms for users vary significantly across platforms. Design/methodology/approach Therefore, this study analyzes the factors affecting technical coping intention based on technical functions of SNSs for users exposed to cybercrime, such as sexual harassment. We divide coping intention into active and passive coping intention. Furthermore, this research focuses on trust as an antecedent of coping intention and verifies how human and system-like trust affects two coping intentions in different directions. Findings Findings reveal that system-like trust significantly affects both active and passive coping intention as a belief in whether the technology will work properly. However, in the case of human-like trust, trust in the platform provider was found to negatively affect passive coping, which is considered unsocialized behavior on SNS platforms. Therefore, both human-like and system-like trust for the platform must be appropriately applied to cope with the problem while activating the platform.

• Journal of Information Science Theory and Practice
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• 제12권1호
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• pp.39-59
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• 2024

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites.