• 유통과학연구
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• 제14권10호
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• pp.165-178
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• 2016

Purpose - This study aims to empirically verify the impact of media richness, social presence, and prior life satisfaction on various continual usage or withdrawal behaviors of SNS users via both a positive path of satisfaction and a negative path of relative deprivation. By identifying these causal paths, we observe dynamic interactions of SNS user psychology in a balanced view, and provide some implications about design principles for SNS providers. Research design, data, and methodology - We developed 16 hypothesis based on media richness theory, social presence theory, social comparison theory, the literature about relative deprivation, and the literature about the various reactions of IS users. The rich SNS media, social presence recognition among peer SNS users, and prior life satisfaction could generate positive experience, attitude, and virtuous behavioral intentions among SNS users. At the same time, rich media, low social presence, and low prior life satisfaction could generate relative deprivation and could increase withdrawal behavioral intentions such as refusal to provide information, misrepresentation of information, and removal of uploaded information in SNS. Scenario surveys were conducted to collect data from potential SNS users. Data from 357 surveys were collected and analyzed through a PLS algorithm to test the hypotheses. Results - Media richness, social presence, and prior life satisfaction could significantly increase perceived enjoyment, satisfaction, and behavioral intention of continual usage and knowledge sharing. They also could significantly decrease refusal and misrepresentation intention. Relative deprivation is significantly decreased only by prior life satisfaction. Relative deprivation could not significantly decrease satisfaction, but it could significantly increase misrepresentation and removal intention, which could be regarded as information distortion intention. Conclusions - SNS providers should focus on developing rich media and social presence support because these two variables could impact the positive experiences of SNS users. Moreover, the positive experiences could heavily influence SNS user behavior. Some management is needed to prevent relative deprivation and its consequences of misrepresentation and removal intention. SNS providers should prevent SNS users from excessive image misrepresentation and removal as this information distortion could be the source of relative deprivation.

• 한국인터넷방송통신학회논문지
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• 제12권6호
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• pp.267-272
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• 2012

IT 융합, 소셜 미디어, 서비스 산업 고도화, 기업들의고객 데이터 수집활동, 멀티미디어 콘텐츠의 폭발적 증가와 스마트폰 보급, SNS 활성화, 사물통신망의 저변확대로 데이터량은 10년 전 산업분야에 걸쳐 고르게 EDW(Enterprise Data Warehouse)의 수요가 증가했었다. 특히 통신업계에서는 KT가 전사적인 EDW를 진행 했고, 산자부도 각각의 업무 부서별로 여러 건의 DW 프로젝트가 진행 하였다. 이외에도 연세의료원, 건국대 병원 등 많은 종합 병원들이 올해 DW의 도입하여 구축을 완료하였다. 그러나 계속 증가되고 있는 데이터와 사용자의 증가는 데이터의 관리에 또 다른 문제점을 만들고 있다. 최근 SNS사용자의 급증과 이를 배경으로한 응용 연구들이 진행되면서 빅데이터를 이용한 새로운 연구를 제안하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.3203-3215
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• 2015

Due to the high growth of SNS population, service scalability is one of the critical issues to be addressed. The cloud environment provides the flexible computing and storage resources for services deployment, which fits the characteristics of scalable SNS deployment. However, if the SNS related information is not properly placed, it will cause unbalance load and heavy transmission cost on the storage virtual machine (VM) and cloud data center (CDC) network. In this paper, we characterize the SNS into a graph model based on the users' associations and interest correlations. The node weight represents the degree of associations, which can be indexed by the number of friends or data sources, and the link weight denotes the correlation between users/data sources. Then, based on the SNS graph, the two-step algorithm is proposed in this paper to determine the placement of SNS related data among VMs. Two k-means based clustering schemes are proposed to allocate social data in proper VM and physical servers for pre-configured VM and dynamic VM environment, respectively. The experimental example was conducted and to illustrate and compare the performance of the proposed schemes.

• Journal of information and communication convergence engineering
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• 제17권4호
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• 한국정보전자통신기술학회논문지
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• 제8권6호
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• pp.544-550
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• 2015

현재, SNS으로 소통되는 의견들이 증가하고 있기 때문에 SNS 메시지로부터 의미 있는 정보를 유추해내는 오피니언 마이닝(Opinion mining) 기술이 중요해지고 있다. 본 논문은 반의어와 부사의 위치에 따라 가중치를 다르게 설정하여 SNS의 감성 정보를 정확하게 추출하는 오피니언 마이닝 기반 SNS 감성 정보 분석 전략(SEIAS, SNS Emotional Information Analysis Strategy)을 제안한다. 제안하는 SEIAS(SNS Emotional Information Analysis Strategy)는 첫째, 오피니언 마이닝 분석에 필요한 감성사전을 구축하고, 둘째, SNS 데이터를 실시간으로 수집하고, 수집된 SNS 데이터와 감성사전를 비교하여 SNS 데이터의 의견값을 산출한다. 특히, 데이터의 의견값을 산출할 때, 반의어, 부사의 위치에 따라 가중값을 다르게 설정함으로써 기존의 SO-PMI와 비교하였을 때 오피니언 분석결과의 정확도를 향상시켰다.

• 정보처리학회논문지C
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• 제18C권4호
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• pp.217-226
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• 2011

최근 다양한 스마트폰이 개발 보급되면서 SNS(Social Network Service)를 사용하는 사용자 또한 급격히 증가하였다. SNS는 기존 모바일 기기에서 수집할 수 있었던 문자 및 통화내역과 같은 단순한 사용자 데이터 외에도 주고 받은 사진 및 동영상, 음성쪽지나 위치 공유, 대화 내역 등 다양한 정보가 저장되어 디지털 포렌식 관점에서 유용한 데이터 획득이 가능하다. 본 논문에서는 최근 많이 사용하고 있는 아이폰을 대상으로 스마트폰에서 이용할 수 있는 SNS 클라이언트와 각 클라이언트 별로 수집할 수 있는 데이터의 종류를 살펴본다. 또, 각 데이터간의 연관관계를 통해 수집된 데이터의 효율적인 분석 방법을 제시한다.

• Journal of Information Technology Applications and Management
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• 제19권4호
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• pp.75-95
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• 2012

This study focuses on examining the determinants of smartphone-based social network services(SNS) use. That is, the study explores the key factors affecting the use of smartphone-based SNS. People who have been using online-based SNS such as Cyworld and Facebook for years are now using mobile-based SNS such as KakaoStory. Under the situation. the study attempts to draw key determinants of smartphone-based SNS use from the studies of TAM, hedonic information systems, and social perspectives. To test the hypotheses, we conducted partial least squares (PLS) analysis using a total of 233 data collected on the users of smartphone-based SNS including KakaoTalk and KakaoStory. The key findings are as follows : first, it is verified that both ease of use and usefulness, two main factors in TAM, had positive effects on smartphone-based SNS use. Second, for enjoyment and escapism considered as the two main factors of hedonic IS characteristics, only the effect of enjoyment on SNS use was significant. Finally, social ties as a factor of social characteristics had the most significant effect on smartphone-based SNS use. The result implies that smartphone-based SNS can be one of the major means of maintaining existing social ties.

• 수산해양교육연구
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• 제26권5호
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• pp.1138-1150
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• 2014

The Purpose of this study was conducted to investigate the factors influencing SNS addiction among university students for providing the basic data to develop SNS addiction prevention program. The data were collected from 363 university students in B & K cities from 2 to 19 September, 2013 and analyzed with t-test, ANOVA and multiple regression by using SPSS 21.0 program. The significant factors of SNS addiction were average using time daily in weekend(${\beta}=.116$, p=.003), SNS using time per connect(${\beta}=.156$, p=<.001), communication motive(${\beta}=.214$, p<.001), non-loneliness motive(${\beta}=.114$, p=.010), social capital(${\beta}=.127$, p=.001), psychological problems(${\beta}=.381$, p<.001). And these factors explained 54.2% of the variance in SNS addiction. In conclusion, the results from this study indicated a need to develop the intervention program to prevent SNS addiction for health promotion of university students.

• 품질경영학회지
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• 제46권2호
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• pp.339-350
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• 2018

Purpose: The purpose of this study is to propose useful suggestions by analyzing the impact of SNS quality and the pressure which comes from SNS usage objectives on SNS discontinuance intention. Methods: We developed a SNS user's discontinuance intention model in terms of SNS quality and pressure of SNS usage. Survey data of SNS users was analyzed using multi-regression analysis for testing hypotheses. Results: We found that information quality plays an important role in lowering the SNS discontinuance intention. In addition, it was founded that pressure of social networking and information processing are positively related with the SNS discontinuance intention. Conclusion: We expect that this research can provide theoretical and practical implications. As for theoretical, this study can suggest the insight on conceptualization of SNS fatigue in the further study. Regarding practical implication, service providers can make their service strategies based on understanding our analysis.

• 디지털융복합연구
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• 제11권5호
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• pp.41-51
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• 2013

미디어를 통한 많은 소셜 데이터가 유통, 활용, 공개 되고 있다. 이 소셜 데이터를 이용한 미디어에 대한 즐거움과 정보의 효율적인 측면만 부각되고, 여기에서 발생되는 지나친 정보 노출과 사용자에 대한 인신 공격적 집단 댓글의 피해 문제는 소흘히 취급되고 있다. 본 연구에서는, 악성 집단 댓글 분석에 의한 SNS 여론 소셜 데이터 분석을 하였다. 소셜 네트워크가 가진 구조적 정보 이용을 통해 분석된 정보 분석 데이터의 양, 즉 SNS 언급 횟수 인 버즈량이 얼마나 많은 사람들에게 배포되고 악용되는가에 대한 문제를 다양한 측정 방법으로 분석하였다.