• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.126-132
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• 2022

At present, due to the rapid spread of smartphones and activation of IoT, malicious codes are disseminated using SNS, or intelligent intrusions such as intelligent APT and ransomware are in progress. The damage caused by the intelligent intrusion is also becoming more consequential, threatening, and emergent than the previous intrusion. Therefore, in this paper, we propose an intelligent intrusion situation-aware reasoning system to detect transgression behavior made by such intelligent malicious code. The proposed system was used to detect and respond to various intelligent intrusions at an early stage. The anticipated system is composed of an event monitor, event manager, situation manager, response manager, and database, and through close interaction between each component, it identifies the previously recognized intrusive behavior and learns about the new invasive activities. It was detected through the function to improve the performance of the inference device. In addition, it was found that the proposed system detects and responds to intelligent intrusions through the state of detecting ransomware, which is an intelligent intrusion type.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.239-249
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• 2022

This study aims to analyze and compare the activating factors of domestic and overseas scuba diving resorts. Our delphi survey was conducted three times in 30 experts who involved in operation and management including scuba diving resort management representative and training team leader. As a result of comparing the activating factors at domestic versus overseas, it was found that common important activating factors included expansion of convenient facilities at public diving places, installation of safety and medical facilities for divers, development of first aid system including AED and oxygen ventilator, requirement of convenient facilities such as water lift and toilet in diving boat, installation of diving boat screw safety system, local boat operation guideline, regular course training program for scuba diving, employment of professional scuba diving instructor and guide, communication and promotion through various SNS portals, promotion by divers' word of mouth, involvement in regional Diving Resort Association, involvement in Korean Diving Association, communication and mutual benefit with local fishing villages, and linkage policy with local tourism industry.

• Smart Media Journal
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• v.10 no.4
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• pp.71-79
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• 2021

One of the social aspects that changes as the use of the Internet becomes widespread is communication in online space. In the past, only one-on-one conversations were possible remotely, except when they were physically in the same space, but nowadays, technology has been developed to enable communication with a large number of people remotely through bulletin boards, communities, and social network services. Due to the development of such information and communication networks, life becomes more convenient, and at the same time, the damage caused by rapid information exchange is also constantly increasing. Recently, cyber crimes such as sending sexual messages or personal attacks to certain people with recognition on the Internet, such as not only entertainers but also influencers, have occurred, and some of those exposed to these cybercrime have committed suicide. In this paper, in order to reduce the damage caused by malicious comments, research a method for improving the performance of discriminate malicious comments through feature extraction based on parts-of-speech.

• Knowledge Management Research
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• v.23 no.3
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• pp.1-22
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• 2022

This study aims to investigate the gamification effect of the badge awards, the most popular gamification process, on users participation behavior. This study also attempts to investigate the effect of tailored gamification, which designs the system of gamification differently based on users' characteristics, focusing on the level of online user information disclosure. For this, we collect and analyze data on 557 users and 1,048,020 answers from StackOverflow, an online Q&A community for developers. The results show that providing a badge is effective for increasing the amount of user participation, whereas providing a goal through the badge is partially effective for increasing the quality of participation. However, the moderating effect of whether users disclose their SNS information on the relationship between badge gaining and participation decrease is not statistically significant. For platform operators, our findings emphasize the importance of gamification design to enhance user engagement effectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.87-89
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• 2022

Recently, as more consumers spend more time at home due to COVID-19, the time spent on digital consumption such as SNS and OTT, which can be easily used non-face-to-face, naturally increased. Since 2019, when COVID-19 occurred, digital consumption has doubled from 44% to 82%, and it is important to quickly and accurately grasp and apply trends by analyzing consumers' emotions due to the rapidly changing digital characteristics. However, there are limitations in actually implementing services using emotional analysis in small systems rather than large-scale systems, and there are not many cases where they are actually serviced. However, if even a small system can easily analyze consumer trends, it will help the rapidly changing modern society. In this paper, we propose a lightweight trend analysis system that builds a learning network through Transfer Learning (Fine Tuning) of the BERT Model and interlocks Crawler for real-time data collection.

• Information Systems Review
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• v.22 no.4
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• pp.59-74
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• 2020

This paper investigates the asymmetric effect of social sentimental on an individual stock price return. For this purpose, four companies such as POSCO, Korean Electricity, AMORE PACIFIC, KIA Motors are chosen from KOSPI listed companies in terms of dataperspective. The main estimation results are as follows: the positive opinions affect only the stock prices return of three companies while the negative opinions affect all of the companies. It shows that positive or negative texts give asymmetric effect on stock price return and the effect of negative opinions is bigger than that of positive opinions. The results imply that investors are more sensitive to the negatives since they have the tendency of loss aversion. Also, it indicates that subjective opinion on SNS can be used as the proxy for the investment sentiment.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.123-138
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• 2024

With the increase in online shopping and delivery food consumption since the pandemic, solving environmental problems caused by single-use packaging has become an important issue. 'Upcycling' is a combination of 'Upgrade' and 'Recycle', and it is the rebirth of obsolete or discarded objects by adding new value to them, and there are currently various upcycled products on the market. In order to activate upcycling, consumers' awareness of the environment and their values for consumption are very important. This study aims to investigate the influence of students' environmental concern, environmental experience, and consumption value on their purchase intention of upcycled products. Based on the results of previous studies on environmental concern, environmental experience, and consumption value, hypotheses were set, and a survey was conducted among university students nationwide to test the hypotheses. The results of this study are as follows First, environmental concern has a significant positive effect on purchase intention of upcycled products. It can be seen that the more environmental concerns such as global warming and waste disposal problems increase, the more positive attitudes toward upcycled products increase. Second, the research hypothesis that environmental knowledge will have a positive effect on the purchase intention of upcycled products is rejected. It was found that environmental knowledge is acquired through environmental education and many SNS, but it does not have a direct effect on the purchase intention of upcycled products. Third, it was found that the consumption value of college students has a positive effect on the purchase intention of upcycled products by increasing their positive perception of upcycled products. Fourth, college students' purchase intention of upcycled products has a positive effect on their behavioral intention to purchase upcycled products. The results of the study provide implications for relevant organizations such as universities and companies to effectively design upcycling-related education. It is also expected to have a positive impact on the use of upcycled products by providing basic information on the characteristics of consumers who purchase upcycled products.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Korean journal of communication and information
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• v.81
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• pp.299-328
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• 2017

This study investigated news comprehension via the social media by comparing the reading of a news story on the news paper. A news story on the social media was suggested to present information in a conversational form, which differs from a traditional reporting style. To compare the different forms of news information presentation, two conditions were created: in a control condition, a news story was written in a traditional reporting form. In the experimental condition, the same news story was constructed in a conversational form. Participants were assigned randomly in one of two conditions. They read the news story and afterwards, they were asked to recall firstly, the core idea of the news story, secondly the whole news story, and finally to answer to the 10 questions that assessed how well they learned from the news story. Participants' responses were content-analyzed and produced six variables, the extent to recall the core idea, the extent to recall the whole story, the extent to recall wrong information, the extent to recall additional information, the extent to recall causally related contents in general, and finally the extent to recall causally related contents in story-specific. Analyses on the six variables revealed that the group in the news paper condition recalled more core idea, the whole story, and additional information than the group in the social media. But the news paper condition recalled less of wrong information than the group in the social media condition. Additionally, the news paper condition learned more than the group in the social media. Regarding the recall of causally related contents, the general causal relationships were recalled more in the group in the social media condition but the story specific causal relationships were recalled more in the group in the news paper condition. The findings seemingly indicated that a traditional news reporting contributes to news story comprehension more than the conversational form. Authors however added discussions and advised that the findings needed to be read under caution.