• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.43-53
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• 2018

Facebook, which has the largest number of users worldwide, has both positive and negative effects on our lives and society. The positive effects include social support from others, relationship building, entertainment, etc. In contrast, Facebook users also experience negative emotions such as tiredness and irritation, resulting in dissatisfaction as well as withdrawal from Facebook. The current study investigates both positive and negative effects of the use of by different demographic characteristics (i.e., age and gender), Facebook usage pattern (i.e., posters vs. lurkers), and Facebook usage time and frequency. The results show that (1) female users (vs. male users) feel higher level of fatigue and display stronger intention to discontinue Facebook. Moreover, (2) posters (vs. lurkers) feel higher level of positive emotions and social support, and stronger intention to continue Facebook. Lastly, (3) heavy users (vs. light users) exhibit higher level of positive emotions and stronger intentions to continue Facebook. This research sheds light on the fact that the characteristics of users affect individuals' intention to discontinue SNS and offers practical implications on the ever-expanding SNS market.

• Korean Security Journal
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• no.29
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• pp.301-336
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• 2011

This exploratory study aims to review the risks and threats of social network services(SNSs), particularly focusing upon the policing perspective. This paper seeks to acknowledge the present risk/danger of SNSs and the very significance of establishing a strategic framework to effectively prevent and/or control criminal misuse of SNSs. This research thus advocates that proactive study on security issues and criminal aspects of SNSs and preventive countermeasures can play a significant role in policing the networked society in the time of digital/internet age. Social network sites have been increasingly attracting the attention of entrepreneurs, and academic researchers as well. In this exploratory article, the researcher tried to define concepts and features of SNSs and describe a variety of issues and threats posed by SNSs. After summarizing existing security risks, the researcher also investigated both the potential threats to privacy associated with SNSs, such as ID theft and fraud, and the very danger of SNSs in case of being utilized by terrorists and/or criminals, including cyber-criminals. In this study, the researcher primarily used literature reviews and empirical methods. The researcher thus conducted extensive case studies and literature reviews on SNSs. The literature reviews herein cover theoretical discussions on characteristics, usefulness, and/or potential danger/harm of SNSs. Through the literature review, the researcher also concentrated upon being able to identify a strategic framework for law enforcement to effectively prevent criminal misuse of SNSs The limitation of this study can be lack of statistical data and attempts to examine previously un-researched area in the field of SNS and its security risks and potential criminal misuse. Thus, to supplement this exploratory study, more objective theoretical models and/or statistical approaches would be needed to provide law enforcement with sustainable policing framework and contribute to suggesting policy implications.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.3
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• pp.155-183
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• 2015

The rapid growth of information technologies and data networks has increased the volume of data generated from scholarly research and the possibilities of re-using and sharing such data. However, there is a serious problem of management and sharing of research data due to the lack of facilitating policies and supporting infrastructure. In particular, few data repositories exist that support systematic collection and sharing of research data in the humanities. In this regard, the Korea Research Memory (KRM) established by the Korea Research Foundation is a rare exception. The purpose of this research is to present specific processes and strategies that can facilitate the data collection, reuse and preservation through the KRM using task analysis and source document gathering as main focal points. In addition, in order for the effective collection and sharing of research data, the following recommendations are proposed: 1) the need for the adoption of data management plan related policies that govern the collection and sharing of research data generated from publicly funded research projects, 2) the need for training and support services for individual researchers and research institutes, 3) the need for training data specialists, and 4) the citation scheme and structure designed for research data.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.539-546
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• 2016

It is over 12 years since the launch of Korea Train eXpress (KTX) services. Demand for the KTX has been on the increase continuously but few studies have been produced related to this phenomenon. KTX passenger demand has been constantly increasing due to influencing factors such as the expansion of network, rise of oil prices, etc. In this study, our main focus is to verify that there are other types of elements that are causing an increase in KTX demand; our approach looks at changes in social and psychological aspect that have occurred due to the reduction of travel time and cost, as well as the imposition of a five-day workweek. In other words, we considered diffusion theory in the marketing area, which affects product selection and purchasing attitudes, as a key factor that is causing passenger demand to increase. That is to say that it is hypothesized that the demand for travel on the KTX has increased due to the train's utility, which is spread by the diffusion effect Therefore, the Bass diffusion model was applied to explain the dramatic increase in KTX passenger demand. Based on this foundation, it was also discussed how certain marketing strategies that incorporate the diffusion effect should be considered variously for sustainable management of rail transportation, while considering a steady passenger demand.

• The Korean Journal of Archival Studies
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• no.39
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• pp.101-138
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• 2014

This study examined the influence and meaning of SNS as the hot topic of our time from the archival perspective and also studied the 'SNS records management'. The many users mean a high accessibility and utilization of SNS, which increase the influence and value of SNS as a record. Politically, SNS is a tool that strengthens the communication among the voters, politicians and the public while economically, it is a window to accept the complaints of the customers and a marketing tool. In addition, the voices of social minorities are also recorded unlike in the traditional media, which makes the SNS record a method to gain the social variety and diversity. SNS is a place of formation of collective memory and collective memory itself. Furthermore, it can play the role of public sphere. It also is a place for generation of 'big data' in an archival sense. In addition, this study has classified the SNS records management into primary and secondary management that include record management entities, subjects, periods, methods, and causes. This study analyzed the history, status, and the meaning of SNS to assess the values and meanings as the preliminary study for the future SNS record management studies.

• Journal of Platform Technology
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• v.11 no.3
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• pp.83-97
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• 2023

This research aims to identify startup trends by analyzing a large number of news articles through semantic network analysis. Using the BIGKinds article analysis service provided by the Korea Press Foundation, 330,628 news articles from 19 newspapers from January 2013 to December 2022 were comprehensively analyzed. The study focused on exploring the changes in key issues over the past decade, considering the impact of the social environment and global economic trends on entrepreneurship. We compared the number of news articles and changes in issues before and after the COVID-19 pandemic, and visualized entrepreneurship trends through frequency analysis, relationship analysis, and correlation analysis. The results of the study showed that the top keywords for entrepreneurship-related words are startup activation and commercialization, and the correlation between COVID-19 and entrepreneurship keywords is almost negligible in a linear sense, but the number of news articles decreased during the pandemic, which has an impact. In particular, the most frequently mentioned keywords are Ministry of SMEs and Startups, place is the United States, and person is limited. The agency was the SBA, and the entrepreneurship sector is more affected by social issues than any other sector, with the important characteristics of increased frequency of prompt access. This study supplies essential basic data for understanding and exploring issues and events related to entrepreneurship and suggests future research topics in the field.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.327-342
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• 2017

In this paper, we introduce an integrated processing system of smart sensor data for IoT service which collects sensor data and efficiently processes it. Based on the technology of collecting sensor data to the development of the IoT field and sending it to the network · Based on the receiving technology, as various projects such as smart homes, autonomous running vehicles progress, the sensor data is processed and effectively An autonomous control system to utilize has been a problem. However, since the data type of the sensor for monitoring the autonomous control system varies according to the domain, a sensor data integration processing system applying the autonomous control system to various different domains is necessary. Therefore, in this paper, we introduce the Smart Sensor Data Integrated Processing System, apply it and use the window as a reference to process internal and external sensor data 1) receiveData, 2) parseData, 3) addToDatabase 3 With the process of the stage, we provide and implement the automatic window opening / closing system "Smart Window" which ventilates to create a comfortable indoor environment by autonomous control system. As a result, standby information is collected and monitored, and machine learning for performing statistical analysis and better autonomous control based on the stored data is made possible.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.