• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.434-441
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• 2016

Using bigdata analysis procedures, the present study sought to review and explore the various aspects of self-disclosure and boundary impermeability of worldwide twitter users. A total of 415 million tweets issued by 54 million users were collected during 6 months and the users of top 10 languages were investigated. And the effect of languages of twitter users on the boundary impermeability, disclosure rate of user profile, profile image, geographical information, URL in profile and user description were analyzed in this study. The results showed that the boundary impermeability and all the self-disclosure rates of twitter users (profile, profile image, geographical information, URL in profile, user description) were significantly (p<0.001) different among language groups of users. The self-disclosure rates and the average points of Portuguese, Indonesian and Spanish users were higher than those of Arabic, Japanese, Turkish and Korean users. The results also showed a positive relationship between boundary impermeability and the number of tweets (including retweets) issued by each users.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.79-89
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• 2015

Recently, with the introduction of social network services, studies that try to make use of them for the various purposes have been actively investigated. In order to proceed with the research that takes advantage of social network services, it is necessary to review the relevant literature and to identify trends in researches. However, the researches of social network are massive amount, so to review the huge amount of relevant research literature is a very difficult task. Therefore, in this study, we analyze systematically the tendency of research related to social network service focusing on Twitter. Especially, we use the SLR (Systematic Literature Review) technique for systematic literature survey and analysis. For the literature survey, we select korean literature resource sites and 243 studies of literature that are surveyed. Studies and analyzes on Twitter in a variety of research studies were also using Twitter data that way beyond the simple question directly.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.97-108
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• 2017

Web 2.0 paradigm and technologies allow users to contribute their information voluntarily and actively to online community. This paper aims to investigate key determinants of information contribution in online communities. We come up with the research model and proposed hypotheses on the basis of intensive literature review on motivation theory, information literacy, and self expression. Using survey response date collected from those who have ever experienced in uploading or providing information on online community such as social media. A total of 262 survey responses were used to test research hypotheses. The results show that self expression motive influences on information representation capability(IRC) and information contribution. The impact of Information literacy on IRC is found to be significant, and IRC is positively related to information contribution. In addition, approval motive is proven to be key determinant of IRC and information contribution. Some practical implications of these findings are discussed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.207-214
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• 2020

Nowadays, the use of smart phones and various electronic devices is increasing, the Internet and SNS are activated, and we live in the flood of information. The amount of information has grown exponentially, making it difficult to look at a lot of information, and more and more people want to see only key keywords in a document, and the importance of research to extract topics that are the core of information is increasing. In addition, it is also an important issue to extract the topic and compare it with the past to infer the current trend. Topic modeling techniques can be used to extract topics from a large volume of documents, and these extracted topics can be used in various fields such as trend prediction and data analysis. In this paper, we inquire the topic of the three-year papers of 2016, 2017, and 2018 in the field of computing using the LDA algorithm, one of Probabilistic Topic Model Techniques, in order to analyze the rapidly changing trends and keep pace with the times. Then we analyze trends and flows of research.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.501-511
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• 2013

Recently, the visualization method of social network service have been only focusing on presentation of visualizing network data, which the methods do not consider an efficient processing speed and computational complexity for increasing at the ratio of arithmetical of a big data regarding social networks. This paper proposes a cloud based on visualization method to visualize a user focused hierarchy relationship between user's nodes on social network. The proposed method can intuitionally understand the user's social relationship since the method uses fuzzy to represent a hierarchical relationship of user nodes of social network. It also can easily identify a key role relationship of users on social network. In addition, the method uses hadoop and hive based on cloud for distributed parallel processing of visualization algorithm, which it can expedite the big data of social network.

• Journal of Fashion Business
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• v.23 no.5
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• pp.81-95
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• 2019

The purpose of this study is to research consumers' perception of the HiSeoul fashion show, which is being used by new designers as a means of promotion, and to propose a strategy for revitalizing new designer brands. This was done in order to secure basic data from fashion consumers, to help guide marketing strategies and promote rising designers. In this research, the consumers' perception of HiSeoul fashion show was verified using text-mining, data refinement and word clouding that was undertaken by TEXTOM3.0. Also, semantic network analysis, CONCOR analysis and visualization of the analysis results were performed using Ucinet 6.0 and NetDraw. "HiSeoul fashion show" was used as the keyword for text-mining and data was collected from March 1, 2018 to April 30, 2019. Using frequency analysis, TF-IDF, and N-gram, it was also shown that consumers are aware of places where shows are held, such as DDP and Igansumun. It was also revealed that consumers recognize rising designer brands, designer's names, the names of guests attending the show and the photo times. This study is meaningful in that it not only confirmed consumers' interest in new designer brands participating in the HiSeoul Fashion Show through big data but also confirmed that it is available as a marketing strategy to boost brand sales. This study suggests using HiSeoul show room to induce consumer sales, or inviting guests that match the brand image to promote them on SNS on the day the show is held for a marketing strategy.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1047-1059
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• 2015

This study will predict risk factors associated with cigarettes in Korea by analyzing the social big data collected from the internet such as blogs, cafes, and SNSes in Korea, using data mining techniques. The key analysis results are as follows. First, when "raising cigarette price"is mentioned online, the negative group (i.e., the proportion of people holding negative views about smoking) increased from 58.6% to 74.8%, and when "lung cancer" is mentioned, it increased to 73.1%. Second, with regard to cigarettes in general, the positive group (i.e., the proportion of people holding positive views about smoking) decreased by 5.6% after the raising of cigarette prices, while the negative group increased by 6.1%. Third, when policies related to "FCTC, raising cigarette price, non-smoking laws, smoking regulations, non-smoking ads, and nonsmoking business" are more frequently mentioned online, the positive group tended to decrease. Finally, when "non-smoking drugs, non-smoking patches, and non-smoking gums" are more frequently mentioned online, the positive group tended to decrease. However, when "electronic cigarettes and supplements" are more frequently mentioned online, the positive group increased.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.