• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Korean Management Science Review
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• v.28 no.3
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• pp.83-94
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• 2011

Microblogging is a Web2.0 technology that is used to manage online interpersonal relationship in SNS. Microblogging service allows the users to publish online brief text updates, usually less than 140~200 characters, sometimes images too. Recently, it becomes more and more popular. There are many reasons, the major one of which is that it can be perfectly combined with mobile. Based on technology acceptance model (TAM) and according to mobile service and microblogging service, this research adds four attributes : Perceived enjoyment, Habit, Mobility, Social Influence. It studies the factors that will impact on the way in which people use rapid developing Mobile Microblogging Service. This research will compare the influence factors and intension to use with the results from other studies with SNS and Mobile Microblogging Service, and then conclude the differences that can be generated in Microblogging.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.345-346
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• 2019

SNS의 다양한 역기능과 함께 중독문제가 사회적 문제로 대두되고 있는 가운데 이미지 기반의 인스타그램이 강세를 보인다. 이에 본 연구는 SNS중에서 이용도가 높은 인스타그램 사용제한 시 사용자의 감정에 미치는 영향과 대안 활동을 파악하기 위한 목적으로 수행되었다. 실험 방법은 인스타그램 1일 5회 이상 이용자 3명을 대상으로 7일간 앱 삭제 및 이용을 제한하고 매일 1인칭 관찰기법인 자기 일기 작성으로 감정변화와 대안 활동을 수집했다. 본 연구의 결과는 사용 빈도수가 높을수록 시간이 흘러도 부정적 감정이 감소하지 않았고 사용 빈도수가 낮을수록 부정적 감정이 점차 감소하였다. 대안 활동으로는 오프라인 활동보다는 온라인 활동이 많았고 여러 종류의 스마트폰 미디어 활동을 한 것으로 나타났다. 이 연구는 나아가 의존도에 따라 부정적 감정소강 소요 시간을 측정하는 연구로 발전될 것을 기대하며 이에 따라 SNS중독성 해결에 필요한 시간, 대안 활동 제시의 연구 초석이 되길 기대한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.7
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• pp.1075-1081
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• 2013

Twitter, Facebook, and KakaoStory are the major SNS in Korea. Social knowledge production is being produced by those services from numerous collaboration and co-participation in those SNS. Wikipedia or Naver JishikIN service was regarded as the representative product of collective knowledge production during the wired internet era. However now at the wireless internet era centered with smart phones, various forms of collective knowledge production would be achieved by connecting to SNS in real-time. In this thesis, the survey data of collective knowledge production for users of three SNS have been compared and analyzed. The difference of the collective knowledge production mechanism among Twitter, Facebook and KakaoStory has been studied and compared through three variables: the motivation of collective knowledge production, the preference of collective knowledge production model, and collective knowledge production cultural perception. As a result of the analysis of the discriminant factors for three SNS user groups, it turns out that the diversity-toward usage motivation, personal contribution motivation, and collective knowledge production tendency perception are the most influential variables. This thesis is of significance in that it unites the value of social science such as social capital and collective knowledge production from the viewpoint of computer science and opens the new chapter of collective knowledge production with the real-time SNS of wireless internet from the wired internet.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.37-45
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• 2022

With the development of the 4th industry, big data using AI is being used in many areas of our lives, and the importance of data is increasing accordingly. In particular, as various services using personal information appear and hacking attacks that exploit them appear in various ways, the importance of personal information management is increasing. Personal information must be managed safely even when collecting, retaining, using, providing, and destroying personal information, and the rights of information subjects must be protected. In this paper, an analysis was performed on the notification of usage history during the protection of the rights of information subjects using the MyData model. According to the Personal Information Protection Act, users must be periodically notified of the use of personal information, so we notify each individual of the use of personal information through e-mail or SNS once a year. It is difficult to understand and manage which company use my personal information. Therefore, in this paper, a personal information usage history notification system model was proposed, and as a result of performance analysis, it is possible to provide the controllability, availability, integrity, source authentication, and personal information self-determination rights.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.2
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• pp.576-588
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• 2015

This study analyzed the influences of elementary students' usage of smartphone, computer and TV on academic attitude. Of the subjects residing in the U city to target of 10 elementary schools from the fourth grade to sixth grade, 865 students were sampled. This research made a frequency analysis and reliability analysis of the obtained date using SPSS 21.0 program were used. Research results are as follows. First, in the smartphone, computer and TV usage status of elementary school, smartphone, computer and TV were used the high frequency with which almost every commonly used, was found to be necessary to take advantage of the time to less than one hour a day, mostly alone, it has been found that a lot of online games, videos and SNS. Second, the use of smartphone, computer and TV were showed a significant effect on all sub-variables of open, self-concept, initiative, responsibility, learning enthusiasm, future orientation, creativity, self-assessment.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• Spatial Information Research
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• v.22 no.4
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• pp.21-38
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• 2014

Since users who search maps conduct their searching using the name they already know or is commonly called rather than formal name of a specific place, they tend to fail to find their destination. In addition, in typical web map service in terms of spatial searching of map. Location information of unintended place can be provided because when spatial searching is conducted with the vocabulary 'nearby' and 'in the vicinity', location exceeding 2 km from the current location is searched altogether as well. In this research, spatial range that human can perceive is calculated by extracting POI date with the usage of twitter data of SNS, constructing spatial relations with existing POI, which is already constructed. As a result, various place names acquired could be utilized as different names of existing POI data and it is expected that new POI data would contribute to select places for constructing POI data by utilizing to recognize places having lots of POI variation. Besides, we also expect efficient spatial searching be conducted using diverse spatial vocabulary which can be used in spatial searching and spatial range that human can perceive.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.305-317
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• 2014

The purpose of this study is to examine sociological characteristics, internet and smartphone use characteristics of adults with Internet Addiction, so that the results will be used for the identification of the adult internet addicts, the planning and performing of prevention, counseling of adult internet addicts. This study is explorative study in order to find a sociological profile of adults with internet addiction. Using the data of survey on internet addiction 4787 adults in the age of 20~39 who used internet at least one time within a week were analysed. The results of this study showed that there were significant differences in sociological characteristics like in gender, age, education, occupation as well as in internet use characteristics like spending hours on internet use, the first year of internet use etc. Thirdly, the addict group reported that internet use hour increased due to smartphone usage, and that they overused SNS. Finally, implications and suggestions for further studies are discussed.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.149-156
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• 2020

This study investigated the key factors affecting online relational intimacy in the context of SNS. Based on the use and gratification theory, self-presentation, relationship formation and information searching were identified as the main needs of SNS usage. These needs were expected to influence online relational intimacy through user satisfaction, subjective well-being, and disclosing information behaviors. The theoretical framework was validated by a longitudinal method. Hypotheses were tested by using the partial least squares to data from 199 Facebook users. Self-presentation and information searching had a significant impact on both user satisfaction and subjective well-being. However, relationship formation did not significantly affect both user satisfaction and subjective well-being. User satisfaction had a significant direct effect only on online relational intimacy. Subjective well-beings played a significant role in enhancing both disclosing information behaviors and online relational intimacy. Finally, it has been found that disclosing information behaviors are a key factor in enhancing online relational intimacy. The results of this study are expected to provide academic and practical implications for the key antecedents of online relational intimacy.