• 한국IT서비스학회지
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• 제13권3호
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• pp.257-274
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• 2014

Social Network Services (SNSs) have grown to be new and promising tools of marketing. By referring to researches done on e-mail viral marketing, this paper operationizes SNS viral marketing effectiveness to accurately reflect marketing success in SNS environment, and tries to identify its affecting factors. As potential affecting factors, fan size, advertisement type, existence of engagement elicitation and incentive are identified. By sampling real advertisement postings on Facebook, we showed that fan size, advertisement type, and engagement elicitation are factors affecting SNS viral marketing success. This research expanded the conventional model of viral marketing into SNS settings to improve understanding on SNS viral marketing. Motivation is discussed as an important factor, and this research showed that viral campaign can be more successful when it triggers internal motivation to engage, but not the external motivation. This research could also be a guide for practitioners on how to post a successful advertisement in SNSs.

• 한국정보시스템학회지:정보시스템연구
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• 제23권1호
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• pp.93-109
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• 2014

This study aims to develop a rich understanding of user goals in user-empowering information technologies which have been dominating part in the information systems environment. A particular focus is on users' goals in a social network site (SNS) which is a typical example of user-empowering technologies. Users conduct various activities in order to achieve diverse goals in SNS. Thus, investigating what goals users pursue in SNS will give insights into understanding the users. We employed the laddering interview technique and means-end chain approach. Interviews of 50 Facebook users were analyzed to produce a hierarchical goal map showing users' goal structure. The map contains 18 goals, including self-reflection, psychological stability, belongingness, improving productivity, and amusement as ultimate goals in SNS. In the map, there are varied routes from activities to ultimate goals in SNS; that is, a complex assembly consisting of activities and goals. The findings call the information systems research community to have more interests in diverse goals and values users seek with technologies.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2013년도 춘계 학술논문 발표대회
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• pp.199-201
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• 2013

In a construction project in which cooperation among project members gets its importance, trust among construction site members should be foundation to enhance team spirit and achievement. This study analyzes the trust network among staff by using social network analysis (SNA) theory for organizations in apartment complex construction sites in Korea. The analysis illustrates higher level of trust for those positioning in the higher positions in hierarchy and having greater experiences. It might be attributable to own features of construction works where skills are acquired by site experience.

• 정보관리연구
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• 제40권2호
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• pp.1-24
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• 2009

이 연구에서는 국내 기관들 간의 웹을 통한 학술커뮤니케이션 분석에서 더 나아가 국가 차원에서 한국과 일본 과학기술 분야 웹 공간에서의 학술적 교류를 파악하는 것을 목적으로, 웹계량학적 방법론과 사회연결망 기법을 적용하여 양 국가의 웹 공간을 분석하였다. 전체적으로 한국이 일본의 과학기술 분야 웹 학술정보를 더 많이 이용하고 있었으며, 한 일 국가간 학술커뮤니케이션을 이끄는 주체는 대학이라고 할 수 있을 정도로 두 나라 모두 대학을 중심으로 많은 링크를 보내거나 받고 있었다. 링크기반 분석에서 한국과 일본이 상대 국가에 대해 의존적인 학문 분야는 서로 다르게 나타났으며, 학문분야별로 상대 국가에 의해 많이 이용되고 있는 학술정보를 생산하는 한국과 일본의 대학 및 연구기관들을 파악할 수 있었다.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.1-6
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• 2023

Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling and cyber bullying on social media messages. Cyber-bullying is the type of abusing someone with the insulting language. Personal abusing, sexual harassment, other types of abusing come under cyber-bullying. Several existing systems are developed to detect the bullying words based on their situation in the social networking sites (SNS). SNS becomes platform for bully someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as facebook, twitter etc. The ETMA is applied on synthetic dataset collected from various data a source which consists of 5k messages belongs to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score and Accuracy.

• 한국정보시스템학회지:정보시스템연구
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• 제18권4호
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• pp.87-103
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• 2009

Although the Internet has been a important communication tool in modem societies, researchers did not pay attention to its' positive impacts on individual's psychological process. The Internet provides users with a unique environment such as visual isolation, non face-to-face communication, and easiness to escape from social influences. This environment enables people to take free action according to their personality and disclose themselves. From the uses and gratification perspective, the current research reveals that individuals with high extraversion are inclined to maintain social networking sites and those with high openness participate in web communities. The findings indicate that individuals' social use of the internet may reflect their personality. This study, also, reveals that controlling the big five personality dimensions, the amount of self-disclosure through ones' social networking site is positively related with subjective happiness. This finding suggests that like confession behaviors in religious or curing facilities, people can use the Internet as a therapeutic or a preventative method for promoting their well-being. Theoretical contributions and practical implications are discussed.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• 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).

• International journal of advanced smart convergence
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• 제10권3호
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• pp.131-142
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• 2021

Recently, there has been an active service that provides customized news to news subscribers. In this study, we intend to design a customized news service system through Deep Learning-based Social Network Service (SNS) activity analysis, applying real news and avoiding fake news. In other words, the core of this study is the study of delivery methods and delivery devices to provide customized news services based on analysis of users, SNS activities. First of all, this research method consists of a total of five steps. In the first stage, social network service site access records are received from user terminals, and in the second stage, SNS sites are searched based on SNS site access records received to obtain user profile information and user SNS activity information. In step 3, the user's propensity is analyzed based on user profile information and SNS activity information, and in step 4, user-tailored news is selected through news search based on user propensity analysis results. Finally, in step 5, custom news is sent to the user terminal. This study will be of great help to news service providers to increase the number of news subscribers.

• Journal of Information Technology Applications and Management
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• 제20권4호
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• pp.191-205
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• 2013

Recently, the topic of predicting interpersonal trust in online social networks is receiving considerable attention, because trust plays a critical role in controlling the spread of distorted information and vicious rumors, as well as reducing uncertainties and risk from unreliable users in social networks. Several trust prediction models have been developed on the basis of transitivity and composability properties of trust; however, it is hard to find empirical studies on whether and how transitivity and composability properties of trust are operated in real online social networks. This study aims to predict interpersonal trust between two unknown users in social networks and verify the proposition on whether and how transitivity and composability of trust are operated in social networks. For this purpose, we chose three social network sites called FilmTrust, Advogato, and Epinion, which contain explicit trust information by their users, and we empirically investigated the proposition. Experimental results showed that trust can be propagated farther and farther along the trust link; however, when path distance becomes distant, the accuracy of trust prediction lowers because noise is activated in the process of trust propagation. Also, the composability property of trust is operated as we expected in real social networks. However, contrary to our expectations, when the path is synthesized more during the trust prediction, the reliability of predicted trust did not tend to increase gradually.