• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.269-286
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• 2013

The advent of Web 2.0 and social media is taking a leading role of emerging big data. At the same time, however, informational dysfunction such as infringement of one's rights and violation of social order has been increasing sharply. This study, therefore, aims at defining malicious usage, identifying malicious feature, and devising an automated method for classifying them. In particular, the rule-based experiment reveals statistically significant performance enhancement.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.5
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• pp.1019-1035
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• 2020

This study examines the millennial generation, who express themselves and share information on social media after experiencing constantly changing 'hot places' (places of interest) in contemporary cities, with the goal of analyzing space consumption behaviors. Data were collected via an Instagram crawler application developed with Python 3.4 administered to 19,262 posts using the term 'hot places' from November 1 and December 15, 2019. Issues were derived from a text mining technique using Textom 2.0; in addition, semantic network analysis using Ucinet6 and the NetDraw program were also conducted. The results are as follows. First, a frequency analysis of keywords for hot places indicated words frequently found in nouns were related to food, local names, SNS and timing. Words related to positive emotions felt in experience, and words related to behavior in hot places appeared in predicate. Based on importance, communication is the most important keyword and influenced all issues. Second, the results of visualization of semantic network analysis revealed four categories in the scope of the definition of "hot place": (1) culinary exploration, (2) atmosphere of cafés, (3) happy daily life of 'me' expressed in images, (4) emotional photos.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.363-384
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• 2024

This study was conducted to examine the user's experiences with the public library in Daejeon using big data analysis, focusing on the text mining technique. To know this, first, the overall evaluation and perception of users about the public library in Daejeon were explored by collecting data on social media. Second, through analysis using online news articles, the pending issues that are being discussed socially were identified. As a result of the analysis, the proportion of users with children was first high. Next, it was found that topics through LDA analysis appeared in four categories: 'cultural event/program', 'data use', 'physical environment and facilities', and 'library service'. Finally, it was confirmed that keywords for the additional construction of libraries and complex cultural spaces and the establishment of a library cooperation system appeared at the core in the news article data. Based on this, it was proposed to build a library in consideration of regional balance and to create a social parenting community network through business agreements with childcare and childcare institutions. This will contribute to identifying the policy and social trends of public libraries in Daejeon and implementing data-based public library operations that reflect local community demands.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.203-204
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• 2018

This research was conducted to identify adolescent problems in internet articles. This research defines adolescent problems as diverse issues related to adolescents and examine how it was dealt in the media to find out how different categories and the aspect of adolescent problems are changing by time. The result of the research was that in 1990's, education policy and family were mainly dealt with when it came to adolescent problems. As the era is changing, adolescent problems were far diversified compared to the past, and each problems are dealt with similar importance. This research is significant in that it does not only examine the social trend adolescent problems but also expand the range of adolescent counselling and utilizes quantitative analysis in considering diversity to provide new information.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.237-249
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• 2015

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.19-29
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• 2016

This paper suggests a method to refine a massive collective intelligence data, and visualize with multilevel sentiment network, in order to understand information in an intuitive and semantic way. For this study, we first calculated a frequency of sentiment words from each movie review. Second, we designed a Heatmap visualization to effectively discover the main emotions on each online movie review. Third, we formed a Sentiment-Movie Network combining the MDS Map and Social Network in order to fix the movie network topology, while creating a network graph to enable the clustering of similar nodes. Finally, we evaluated our progress to verify if it is actually helpful to improve user cognition for multilevel analysis experience compared to the existing network system, thus concluded that our method provides improved user experience in terms of cognition, being appropriate as an alternative method for semantic understanding.

• Journal of Korean Academy of Nursing
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• v.54 no.2
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• pp.266-278
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• 2024

Purpose: This study aimed to investigate healthcare consumers' interest in patient safety on social media using structural topic modeling (STM) and to identify changes in interest over time. Methods: Analyzing 105,727 posts from Naver news comments, blogs, internet cafés, and Twitter between 2010 and 2022, this study deployed a Python script for data collection and preprocessing. STM analysis was conducted using R, with the documents' publication years serving as metadata to trace the evolution of discussions on patient safety. Results: The analysis identified a total of 13 distinct topics, organized into three primary communities: (1) "Demand for systemic improvement of medical accidents," underscoring the need for legal and regulatory reform to enhance accountability; (2) "Efforts of the government and organizations for safety management," highlighting proactive risk mitigation strategies; and (3) "Medical accidents exposed in the media," reflecting widespread concerns over medical negligence and its repercussions. These findings indicate pervasive concerns regarding medical accountability and transparency among healthcare consumers. Conclusion: The findings emphasize the importance of transparent healthcare policies and practices that openly address patient safety incidents. There is clear advocacy for policy reforms aimed at increasing the accountability and transparency of healthcare providers. Moreover, this study highlights the significance of educational and engagement initiatives involving healthcare consumers in fostering a culture of patient safety. Integrating consumer perspectives into patient safety strategies is crucial for developing a robust safety culture in healthcare.

• 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.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.773-786
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• 2017

This study was to analyze data of climate change impacts on social infrastructure using text-mining methodology, and to visualize the spatial information by integrating those with regional data layers. First of all, the study identified that the following social infrastructure; power, oil and resource management, transport and urban, environment, and water supply infrastructures, were affected by five kinds of climate factors (heat wave, cold wave, heavy rain, heavy snow, strong wind). Climate change impacts on social infrastructure were then analyzed and visualized by regions. The analysis resulted that transport and urban infrastructures among all kinds of infrastructure were highly impacted by climate change, and the most severe factors of the climate impacts on social infrastructure were heavy rain and heavy snow. In addition, it found out that social infrastructure located in Seoul and Gangwon-do region were relatively largely affected by climate change. This study has significance that atypical data in media was used to analyze climate change impacts on social infrastructure and the results were translated into spatial information data to analyze and visualize the climate change impacts by regions.

• Knowledge Management Research
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• v.22 no.3
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• pp.31-53
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• 2021

As a communication channel for individuals, social media is affecting various areas such as business, economy, politics, and society. One of the less-studied areas is the law. Therefore, this study collected various information from social media and analyzed its impacts on the legal decisions, especially the Supreme Court decisions in Korea. This study was conducted by compiling information from Internet news articles and public responses. We found that when the negative reactions from the public got higher, the trial duration until the supreme court making the final decisions became shorter. However, we were not able to find the significant relationship between social media reactions and dismissal of appeal nor annulment. Our study would contribute to the information systems and knowledge management research in a sense that the social analytics is applied to the area of legal decisions, instead of using conventional qualitative study methodology. Our study is also meaningful to the practitioners because that big data analytical business can be applied to the field of law by creating a new database for the emerging legal technology. Finally, law makers can think of a better way to standardize the legal decision process to minimize the reverse effects from social media.