• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3842-3861
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• 2019

In cut throat competition, organizations all over the world try to utilize the immense opportunities the Internet has to offer in almost all of their operations. Today, Social networking sites (SNSs) provide utilities designed to help companies to recruit right personnel. Considering the implication of social media for recruitment at infancy stage in Pakistan and lack of studies related to it, this study aims to investigate the role of different SNSs qualities (Easily navigate, Secure process, Eminence Proficiency, Candidate's Attraction and Network Expedition) in effective E-recruitment (EER). Data were collected through structured questionnaire from employees and managers of major telecom companies of Pakistan. Finally, the result of 355 returned and valid questionnaire with 55% response rate show that the relationship between SNSs qualities and EER is significant. Moreover, results also prove that EER is better than the tradition recruitment and a SNSs comparison show that Facebook is more effective than LinkedIn for EER. The results of this study will help Pakistani companies to develop a successful e-HRM and EER strategy in the current scenario.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.421-429
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• 2022

Entrepreneurship research has focused on several factors that might affect the intention of an individual to start an enterprise. Using principles from social network theory and the entrepreneurial intention model (EI), the current research intends to investigate how social capital is formed on Social Networking Sites (SNS) and how the resulting social capital influences entrepreneurial intention. Using an online survey, 151 valid responses were received from university students. Applying partial least square structural equation modeling, positive and significant relationship was found between the SNS usage and bonding and bridging social capital. Further, it was also found that online-bonding social capital does not impact any of the three antecedents of entrepreneurial intention. On the other hand, online-bridging social capital significantly influences personal attitudes and subjective norms. It was also found that both personal attitude and perceived behavioral control significantly relate to EI, while the subjective norms do not relate significantly to EI. The paper contributes to the literature on technology-based human behavior and entrepreneurship in emerging countries, opening some areas for future research, while also providing some managerial insights. It also should be beneficial to educational institutions in understanding how the use of SNS use by students may be optimized.

• 여성건강간호학회지
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• 제17권2호
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• pp.157-168
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• 2011

Purpose: This study was done to examine differences in mammography screening according to breast cancer and social network characteristic. Methods: Data were collected from 187 married women 35 years and older who were using public health centers, health promotion centers, cultural centers, obstetrics and gynecology hospitals or other relevant community sites. Data were collected between October 24 and December 4, 2008. Data were analyzed using the SPSS/WIN 15.0 program. Results: The participation rate for mammography screening was 35.3%. The following general and breast cancer characteristics showed statistically significant differences: religion, family incomes, regular medical-care, general health examinations during past 2 years, and history of breast disease. The following social network characteristics showed statistically significant differences: social norms and subjective norms. Using logistic regression analysis, regular medical-care, breast cancer risk appraisal, social norm, and subjective norms were highly predictive of subsequent mammography. Conclusion: The results of this study indicate that it is important to develop and provide tailored intervention programs through integrated socially mediated programs. By consciously including social network and support systems, breast cancer detection efforts would not end as a one-time event, but naturally build on network structure of adults women, thus facilitating regular mammography screening.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4191-4211
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• 2019

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

• 지능정보연구
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• 제17권2호
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• pp.39-60
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• 2011

웹 2.0 기술이 소개된 이후 소셜 네트워크 서비스는 미래 정보기술의 기초로서 중요하게 인식되고 있다. 이에, 웹2.0 환경에서 소셜 네트워크를 구축하기 위하여 온톨로지 기반의 사용자 프로필 기술 도구인 FOAF를 활용하기 위한 다양한 연구가 이뤄지고 있다. 그러나 FOAF를 이용하여 소셜 네트워크를 생성 및 관리하는 대부분의 방법은 시간의 흐름에 따라 변화하는 사용자의 소셜 네트워크를 자동적으로 반영하기 어려운 단점이 있으며 다양한 소셜 미디어 서비스가 제공되는 환경에서는 FOAF를 동적으로 관리하기가 쉽지 않다. 따라서 본 논문에서는 기존 FOAF를 이용한 소셜 네트워크 추출방법의 한계를 극복하기 위하여 사용자 프로파일 기술 언어인 FOAF와 웹 저작물 출판 매커니즘인 RSS를 OLAP 시스템에 적용시켜 동적으로 FOAF를 갱신하고 관리하기 위한 방법을 제안한다. 본 논문에서 제안하는 방법은 수집한 FOAF와 RSS 파일들을 스타스키마로 설계된 데이터베이스에 넣어 OLAP 큐브를 생성한다. 그리고 OLAP 연산을 이용하여 사용자의 연결관계를 분석하고 FOAF에 그 결과를 반영한다. 본 논문에서 제안하는 방법은 이기종 분산처리 환경 하에서 데이터의 상호호환성을 보장할 뿐만 아니라 시간의 흐름에 따른 사용자의 관심 및 이슈 등의 변화를 효과적으로 반영한다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제2권2호
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• pp.101-107
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• 2021

The objective of this study was to analyze social wasps' urban distribution tendency based on 10 species found in two metropolitan cities (Busan and Daegu) of South Korea. There 10 species included six species (Vespa mandarinia, V. ducalis, V. crabro flavofasciata, Vespula koreensis koreensis, Parapolybia indica, and Polistes snelleni) of forest dwellers that inhabited urban main forests and satellite forests, two species (V. simillima simillima and V. analis parallela) of facultative dwellers that nested at diverse sites of urban areas with greater preference for urban forest, and two species (V. velutina nigrithorax and P. rothneyi koreanus) of urban dwellers that nested at almost all sites, including urban and forest areas. These urban dwellers were found to adapt well to an urban environment based on their far higher rate of urban nesting compared to facultative dwellers. When distribution tendencies of facultative dwellers and urban dwellers in Busan and Daegu were compared, a regular distribution was mostly observed in Busan with a dense forest network. For Daegu that lacked forest connectivity, the greatest distribution of species was found in the nearby urban forest. For Daegu, a city further away from forests, urban dwellers occurred far beyond forest sites compared to Busan with a dense forest network.

• 복식문화연구
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• 제23권3호
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• pp.498-511
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• 2015

This paper examines advertising images of fashion brands in vertical social network site (SNS) from the viewpoints of message strategies. Vertical social network sites are types of social curation systems applied to social networking, where information is selected, organized, and maintained. Fashion brands communicate with consumers by presenting images on vertical SNSs, anticipating improvements in brand image, popularity, and loyalty. Those images portray content for particular brands and seasonal concepts, thus creating paths for product sales information. Marketing via SNSs corresponds to relationship marketing, which refers to long-term interrelationship and value augmentation between the company and consumer, and viral advertising, which relies on word of mouth distribution via social network platforms. Taylor's six-segment message strategy wheel, often used for analyzing viral ads, was applied to conduct a content analysis of the images. A total of 2,656 images of fashion brands advertised on Instagram were selected and analyzed. Results indicated that brand values were somewhat related to the number of followers. Follower rankings and comment rankings were also correlated. In general, fashion brands projected sensory messages most often. Acute need and rational messages were less common than other messages. Sports brands and luxury brands presented sensory messages, whereas fast fashion brands projected routine images most often. Fashion brands promoted on vertical SNSs should portray advertising images that combine message strategies

• International Journal of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.113-119
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• 2020

Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.

• 응용통계연구
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• 제24권6호
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• pp.1169-1179
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• 2011

본 논문에서 포털사이트의 정보를 이용한 사회연결망분석을 통하여 통계학 책에서 주로 언급되는 확률분포들의 종류와 쓰임새에 대한 설명이 일상생활에서 언급되는 확률분포들과 어떤 관계가 있는 지 알아본다. 이를 통하여 우리들의 일상생활을 염두에 둘 때 통계학 책에서 강조하여야 할 확률분포들에 대하여 알아본다.