• Proceedings of the Korean Geotechical Society Conference
• /
• 2009.09a
• /
• pp.835-844
• /
• 2009

In this study, a database composed by 46 cases of tunnel collapses has been built up. Based on the database, comprehensive data analysis is carried out, providing us a number of the technical lessons, which can be considered in future design and construction to minimize possibility of tunnel collapse disaster. For making a better understanding, the technical lessons are given in two divisions: mountain tunnel and urban tunnel. Tunnel collapses taking place in the former tunnel are generally due to bad discontinuity condition of jointed rock mass. Otherwise, urban tunnel has weak condition generally on ground water and weathering of ground. Most of technical comments given in this paper are made based on the cases of tunnel collapses only used in this study, so that the comments seems to be hard to be available to all the tunnelling cases. However, the comment should be valuable technical lessons for tunnel engineers to consider in tunnel design or construction.

• Korea Journal of Hospital Management
• /
• v.4 no.2
• /
• pp.60-84
• /
• 1999

Hospitals under competitive environment are getting more interested in PR(public relations) as a means of marketing. A typical form of public relations for the hospital is publicity, and its principal instrument is the press release that provides the mass media with the raw material and background for a news story. The purpose of this paper is to examine and analyze the public relations articles associated with hospitals in the section of health care in major daily newspapers. Three major daily newspapers, Chosun, Joongang and Hankyoreh have been analyzed for a year from January 1, 1998 to December 31, 1998. All articles in the health care section are classified by content, size, press comments, and style. This study has found some perverse aspects of the public relations such as the preponderance of health articles on tertiary medical institutions, inappropriate publicity focused on certain medical treatments, doctors, and hospitals, and the positive press comments on the hospitals that are affiliated with the same corporate group as the newspaper.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.1
• /
• pp.9-15
• /
• 2018

Policy polls, which investigate the degree of support that the policy has for policy implementation, play an important role in making decisions. As the number of Internet users increases, the public is actively commenting on their policy news stories. Current policy polls tend to rely heavily on phone and offline surveys. Collecting and analyzing policy articles is useful in policy surveys. In this study, we propose a method of analyzing comments using deep learning technology showing outstanding performance in various fields. In particular, we designed various models based on the recurrent neural network (RNN) which is suitable for sequential data and compared the performance with the support vector machine (SVM), which is a traditional machine learning model. For all test sets, the SVM model show an accuracy of 0.73 and the RNN model have an accuracy of 0.83.

• East Asian mathematical journal
• /
• v.37 no.5
• /
• pp.599-612
• /
• 2021

In China, Weibo is one of the social platforms with more users. It has the characteristics of fast information transmission and wide coverage. People can comment on a certain event on Weibo to express their emotions and attitudes. Judging the emotional tendency of users' comments is not only beneficial to the monitoring of the management department, but also has very high application value for rumor suppression, public opinion guidance, and marketing. This paper proposes a two-input Adaboost model based on TextCNN and BiLSTM. Use the TextCNN model that can perform local feature extraction and the BiLSTM model that can perform global feature extraction to process comment data in parallel. Finally, the classification results of the two models are fused through the improved Adaboost algorithm to improve the accuracy of text classification.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.1-5
• /
• 2021

Cyberbullying is a problem that is faced in many cultures. Due to their popularity and interactive nature, social media platforms have also been affected by cyberbullying. Social media users from Arab countries have also reported being a target of cyberbullying. Machine learning techniques have been a prominent approach used by scientists to detect and battle this phenomenon. In this paper, we compare different machine learning algorithms for their performance in cyberbullying detection based on a labeled dataset of Arabic YouTube comments. Three machine learning models are considered, namely: Multinomial Naïve Bayes (MNB), Complement Naïve Bayes (CNB), and Linear Regression (LR). In addition, we experiment with two feature extraction methods, namely: Count Vectorizer and Tfidf Vectorizer. Our results show that, using count vectroizer feature extraction, the Logistic Regression model can outperform both Multinomial and Complement Naïve Bayes models. However, when using Tfidf vectorizer feature extraction, Complement Naive Bayes model can outperform the other two models.

• Journal of Fashion Business
• /
• v.26 no.6
• /
• pp.116-132
• /
• 2022

Fashion marketing activity performed by social media influencers (SMIs) has grown exponentially in the past years. Research regarding their marketing power is often discussed in the context of sponsorship disclosure, in order to overcome obstacles of impending regulations that may endanger the authenticity SMIs are perceived to have compared to traditional marketing agents. Upon recent heterogeneous findings, a netnographic approach was taken to examine the actual sponsored posts of SMIs on Instagram. Based on two representative cases that received media coverage, a qualitative analysis of 1,058 comments on Instagram showed customer inspiration and informational social influence regardless of sponsorship disclosures present. Regarding customer inspiration, high frequency of comments including choice imitation intentions was observed. Under certain conditions, customer responses were focused on the SMI's expertise irrespective from the brand being endorsed. Findings show future implications from both an academic and industry-focused perspective for future potential of SMIs in fashion marketing.

• Journal of Information Processing Systems
• /
• v.19 no.5
• /
• pp.641-651
• /
• 2023

Malicious hate speech and gender bias comments are common in online communities, causing social problems in our society. Gender bias and hate speech detection has been investigated. However, it is difficult because there are diverse ways to express them in words. To solve this problem, we attempted to detect malicious comments in a Korean hate speech dataset constructed in 2020. We explored bidirectional encoder representations from transformers (BERT)-based deep learning models utilizing hyperparameter tuning, data sampling, and logits ensembles with a label distribution. We evaluated our model in Kaggle competitions for gender bias, general bias, and hate speech detection. For gender bias detection, an F1-score of 0.7711 was achieved using an ensemble of the Soongsil-BERT and KcELECTRA models. The general bias task included the gender bias task, and the ensemble model achieved the best F1-score of 0.7166.

• Asia pacific journal of information systems
• /
• v.30 no.2
• /
• pp.420-438
• /
• 2020

Recently, with the advance of the wireless Internet access via mobile devices, a myriad of game development companies have forayed into the mobile game market, leading to intense competition. To survive in this fierce competition, mobile game developers often try to get a grasp of the rapidly changing needs of their customers by operating their own official communities where game users freely leave their requests, suggestions, and ideas relevant to focal games. Based on the heuristic-systematic model (HSM) and the data quality (DQ) framework, this study derives key content, non-content, and hybrid cues that can be utilized when game developers accept suggested postings in these online communities. The results of hierarchical multiple regression analysis show that relevancy, timeliness, amount of writing, and the number of comments are positively associated with mobile game developers' knowledge adoption. In addition, title attractiveness mitigates the relationship between amount of writing/the number of comments and knowledge adoption.

• Asia pacific journal of information systems
• /
• v.29 no.3
• /
• pp.489-502
• /
• 2019

The WeChat mini program is an application embedded in WeChat that users can use without downloading and installing. After it was officially released in 2017, many travel enterprises have launched their own mini programs on the WeChat platform. This study applies affordance theory to investigate the WeChat mini program's role in tourism activities through social network analysis using the R programming language. The authors searched the topic of "how do you perceive the travel-related WeChat mini program" and then crawled the 200 comments found; 180 comments were analyzed after data cleansing. The results show that travel-related WeChat mini programs play a very important role in Chinese social network tourism activities. This paper found that WeChat played a more active role in various tourism-related interactions with Chinese social networks. Moreover, the results show how affordance theory is applied to the use of WeChat mini programs.

• Journal of Chest Surgery
• /
• v.47 no.6
• /
• pp.573-573
• /
• 2014