• The Journal of Information Systems
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• v.24 no.3
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• pp.21-46
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• 2015

Purpose Social Network Service (SNS) users feel fatigue in process of using open type of SNS like Facebook and Twitter. Compared to the open SNS, the closed SNS takes an closed form to prevent privacy exposure, and they are more practical and advantageous to form deeper social relationships. This study attempt to examine the effects of the mobile closed SNS characteristics (such as usefulness, playfulness, perceived security, psychological privacy, social influence, and belonging) on the users' continuous SNS usage intention. Design/methodology/approach This study used a mixed methodology combining in-depth interviews and empirical validation to investigate the effects of the mobile closed SNS characteristics on the continuous SNS usage intention of users. Findings Analytical results from a survey of 210 mobile closed SNS users showed that except perceived security, the effects of the five SNS characteristics on continuous SNS usage intention were significant. These findings contribute to improving the quality of mobile closed SNS services and suggesting SNS related marketing strategies.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.43-50
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• 2015

Since the popularity of social network services (SNS) rise, the data produced from them is rapidly increased. The SNS data includes personal propensity or interest and propagates rapidly so there are many requests on analyzing the data for applying the analytic results to various fields. New technologies and services for processing and analyzing big data in the real-time are introduced but it is hard to apply them in a short time and low coast. In this paper, an efficient method to build a tweet analysis system without inducing new technologies or service platforms for handling big data is proposed. The proposed method was verified through building a prototype monitoring system to collect and analyze tweets using the MySQL database and the PHP scripts.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1014-1028
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• 2017

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of Information Technology Services
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• v.21 no.5
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• pp.95-106
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• 2022

Recently, physical examinations have become an important strategy to reduce costs for individuals and society. Pre-physical counseling is important for an effective physical examination. However, incomplete counseling is being conducted because the demand for physical examinations is not predicted. Therefore, in this study, the demand for physical examination was predicted using text mining and stepwise regression. As a result of the analysis, the most recent text data showed a high explanatory power of the demand for physical examination. Also, large amounts of data have high explanatory power. In addition, it was found that the high frequency of the text "health food" reduces the number of health examination customers. And the higher the frequency of the text of the word "food", the lower the number of physical examination customers. However, when the word "wild ginseng" was exposed a lot on Twitter, the number of physical examination customers visiting hospitals increased. In other words, customers consume efficiently by comparing the health examination price with the price of consumer goods. The proposed research framework can help predict demand in other industries.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.263-285
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• 2016

Nowadays, Social Network Service (SNS), which has been in the spotlight as a way of communication, has become a most effective tool to improve easy of information use and accessibility for users. In this paper, we chose Twitter as the most representative SNS services because of automatic crawling and investigated tweet data gathered from domestic and foreign National Archives - NARA of U.S.A., TNA of U.K.. NAA of Australia, and National Archives of Korea. We also conducted information genres analysis and trend analysis by timeline. Information genres analysis shows how archives satisfied users' information needs as well as trends analysis of tweets helps to understand how users' interestedness was changed. Based on comparison results, we distilled four characteristics of National Archives and suggested vitalization ways for National Archives of Korea.

• Convergence Security Journal
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• v.17 no.1
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• pp.63-68
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• 2017

The Jasmine Revolution started from Tunisia in January 2011 has brought many changes into countries in North Africa and Middle East. We need to study the causes of the revolution. First, the kings and dictators in those countries oppressed the opposition parties and the media aiming for long-term seizure of power. The power concentrated on specific people produced illegalities and corruption. Secondly, most of the national income of those countries belonged to kings and dictators producing problems during the distribution of the income. Especially, with the decrease of oil price in 1990s and the increase of the price of daily necessities in 2000s people lost their credits on their governments. Lastly, the number of people in those countries using the Internet has increased by 4,863% from 2000 to 2010. The expansion of social network services such as Facebook and Twitter was one of factors that made the information control by those countries difficult. We should think about the possibility of sudden change in North Korea. It is necessary to compare and analyze the political, economic and social characteristics between those countries and North Korea. It shouldn't be just a simple comparison or analysis. It should provide basic data for objective and quantified index development in relation to sudden change in North Korea.

• Journal of Korean Academy of Nursing
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• v.47 no.6
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• pp.806-816
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• 2017

Purpose: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. Methods: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. Results: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. Conclusion: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

• Smart Media Journal
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• v.6 no.3
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• Journal of Korea Entertainment Industry Association
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• v.5 no.4
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• pp.147-153
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• 2011

On 11 March 2011, earthquake occurred in Japan were left with the significant human and material damage. Among these, social media has emerged as a very useful communication channel by playing a important roles in the process of safety checking and disaster recovery. In fact, however, we have no specific response methods or systems to these occurrence of disaster in Korea. Therefore, we investigate the relevant literature in order to analyze the current status of foreign and domestic utilization of social media, and draw the ideal ways to use social media for responding to unpredictable disasters based on the relevant literature reviews. The results of this study are summarized as follows. First, Institutions such as Meteorological Administration or National Emergency Management Agency are able to utilize social media as the communication systems to public in emergency. Second, social media are able to used for building disaster information systems including location information such as emergency call for help or requests for dissemination materials. Lastly, online donation services via Facebook or Twitter are able to be provided. The findings have significant implications for official responsible for disaster response and academic researchers.