• Journal of the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.12-29
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• 2024

The purpose of this study is to identify the social issues discussed in Korea regarding Maritime Autonomous Surface Ships (MASS), the most advanced ICT field in the shipbuilding industry, and to suggest policy implications. In recent years, it has become important to reflect social issues of public interest in the policymaking process. For this reason, an increasing number of studies use media data and social media to identify public opinion. In this study, we collected 2,843 domestic media articles related to MASS from 2017 to 2022, when MASS was officially discussed at the International Maritime Organization, and analyzed them using text mining techniques. Through term frequency-inverse document frequency (TF-IDF) analysis, major keywords such as 'shipbuilding,' 'shipping,' 'US,' and 'HD Hyundai' were derived. For LDA topic modeling, we selected eight topics with the highest coherence score (-2.2) and analyzed the main news for each topic. According to the combined analysis of five years, the topics '1. Technology integration of the shipbuilding industry' and '3. Shipping industry in the post-COVID-19 era' received the most media attention, each accounting for 16%. Conversely, the topic '5. MASS pilotage areas' received the least media attention, accounting for 8 percent. Based on the results of the study, the implications for policy, society, and international security are as follows. First, from a policy perspective, the government should consider the current situation of each industry sector and introduce MASS in stages and carefully, as they will affect the shipbuilding, port, and shipping industries, and a radical introduction may cause various adverse effects. Second, from a social perspective, while the positive aspects of MASS are often reported, there are also negative issues such as cybersecurity issues and the loss of seafarer jobs, which require institutional development and strategic commercialization timing. Third, from a security perspective, MASS are expected to change the paradigm of future maritime warfare, and South Korea is promoting the construction of a maritime unmanned system-based power, but it emphasizes the need for a clear plan and military leadership to secure and develop the technology. This study has academic and policy implications by shedding light on the multidimensional political and social issues of MASS through news data analysis, and suggesting implications from national, regional, strategic, and security perspectives beyond legal and institutional discussions.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.273-278
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• 2016

Disasters are linked with some attributes of modern society in diversity, complexity and unpredictability, so various approach and remedies of them will appease the nation's anxiety upon them. For this reason, in this paper we review the building plans and its application and its application Province pros and cons of intelligent video surveillance analysis system for social security, the future proposed service is the safety of people while performing a comprehensive urban traffic control functions as a video monitoring system for social security and to ensure, to prevent crime and accidents, it is to crack down on criminal offenses in advance expected to protect the property of citizens and public facilities.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.199-204
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• 2022

The purpose of this study is to broaden the understanding of the Security Services Industry Act, and also to examine the meanings of various phenomena by analyzing the media report big data rather than the researchers' perspective on the Security Services Industry Act. In the research method, this study searched for a keyword 「Security Services Industry Act」 that prescribes the security work as an important subject of crime prevention and maintenance of public order in Korea. The data was searched from 1990 to 2021 the BIG KINDS could provide. Also, for the concrete analysis during the period of data search, it was divided into settlement period(1976~2001), growth period-quantitative(2002~2012), and growth period-qualitative(2013~2021). In the results of this study, the media report perception of the Security Services Industry Act is continuously emphasizing the social roles and importance of private security according to the flow of time. The consequent marketability of private security will play great roles in the protection of people's lives and properties in the combination with various other industries in the future. However, the private security industry that provides public peace service together with the police, could be rising as an element that hinders the development of private security industry because of various social issues caused by legal regulations and illegal problems, so it would be necessary to more strengthen its responsibility and roles accordingly.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.277-284
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• 2020

Text mining is a representative method of big data analysis that extracts meaningful information from unstructured and large amounts of text data. Social media such as Twitter generates hundreds of thousands of data per second and acts as a one-person media that instantly and directly expresses public opinions and ideas. The traditional media are delivering informations, criticizing society, and forming public opinions. For this, we compare the responses of SNS with the responses of media on the issue of the termination of the Korea-Japan GSOMIA (General Security of Military Information Agreement), one of the domestic issues in the second half of 2019. Data collected from 201,728 tweets and 20,698 newspaper articles were analyzed by sentiment analysis, association keyword analysis, and cluster analysis. As a result, SNS tends to respond positively to this issue, and the media tends to react negatively. In association keyword analysis, SNS shows positive views on domestic issues such as "destruction, decision, we," while the media shows negative views on external issues such as "disappointment, regret, concern". SNS is faster and more powerful than media when studying or creating social trends and opinions, rather than the function of information delivery. This can complement the role of the media that reflects public perception.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.37-48
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• 2022

With development of Digital Technology, social issues are communicated through digital-based platform such as SNS and form public opinion. This study attempted to analyze big data from Twitter, a world-renowned social network service, and find out the public opinion. After collecting Twitter data based on 14 keywords for 1 year in 2021, analyzed the term-frequency and relationship among keyword documents with pearson correlation coefficient using Data-mining Technology. Furthermore, the 6 main topics that on the center of information security field in 2021 were derived through topic modeling using the LDA(Latent Dirichlet Allocation) technique. These results are expected to be used as basic data especially finding key agenda when establishing strategies for the next step related industries or establishing government policies.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.295-305
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• 2016

The purpose of this study is to analyze the internal reputation factors that affect the job satisfaction by big data analysis in the social media for corporate reputation and verify the difference between large corporations and small-medium corporations for each factor of internal reputation. The result showed 'Salaries and Benefits' is a major factor that affects the job satisfaction for all research corporations, 'Senior Management' is a major factor for large corporations, and 'Salaries and Benefits' is a major factor for small-medium corporations. As for the difference factors of large corporations and small-medium corporations are 'Job Satisfaction', 'Salaries and Benefits', and 'Work-life Balance'. Unstructured data analysis shows some interesting features to be studied further.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.396-404
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• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.109-118
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• 2024

With the increasing number of social media users worldwide, cases of social media being abused to perpetrate various crimes are increasing. Specifically, drug distribution through social media is emerging as a serious social problem. Using social media channels, the curiosity of teenagers regarding drugs is stimulated through clever marketing. Further, social media easily facilitates drug purchases due to the high accessibility of drug sellers and consumers. Among various social media platforms, we focused on Instagram, which is the most used social media platform by young adults aged 19 to 24 years in South Korea. We collected four types of information, including profile photos, introductions, posts in the form of images, and posts in the form of texts on Instagram; then, we analyzed the similarity among each type of collected information. The profile photos and posts in the form of image were analyzed for similarity based on the SSIM(Structural Simplicity Index Measure), while introductions and posts in the form of text were analyzed for similarity using Jaccard and Cosine similarity techniques. Through the similarity analysis, the similarity among various accounts for each collected information type was measured, and accounts with similarity above the significance level were determined as the same drug sales account. By performing logistic regression analysis on the aforementioned information types, we confirmed that except posts in image form, profile photos, introductions, and posts in the text form were valid information for tracking the same drug sales account.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.77-84
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• 2022

The study highlights the features and trends of digital marketing for international business. To achieve these goals, the authors used a systematic approach that allows a comprehensive approach to the object of study, as well as used general and specific methods of scientific knowledge on the application of digital marketing for international business. The dynamics of the number of users of social networks in the world is analyzed, which allowed us to conclude about the steady trend of increasing the number of users of the Internet and social networks, as well as the time spent by users on social networks. The study of the dynamics of the number of users of social networks provides increased efficiency in the use of digital marketing tools to enhance international business. The most effective digital marketing tools for international business, including artificial intelligence, conversational marketing, chatbots, personalization, video marketing, live shopping, social media stories, interactive content, omnic marketing, augmented reality and technology immersion, native advertising, green marketing and mobile commerce.