• Journal of the Korea Convergence Society
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• v.7 no.2
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• pp.19-24
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• 2016

On a huge space of information called the Internet, users can use a smart mobile web to get information on various intellectual fields and can access to various Medias such as personal blogs and social networking sites (SNS). This is why a vast amount of information on the web has been effectively managed and researched nowadays through a technology named Semantic Web. However, it still needs for an improvement for studies on searching for intellectual information, though it is enhanced to integrate variously spread information and search for intellectual information user-oriented. Thus, this study aims to research on searching information and knowledge spread around a knowledge-filled information space, which can improve credibility according to user-oriented logic.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.207-214
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• 2013

It was the 18th presidential election TV debate Twitter participation of SNS. Began to diverge as the era of social media, combined with SNS through in the mass media, media web 2.0. Search tweets, retweets, while the formation of policy issues, the agenda of Twitter users to listen to the statements of the candidates using the Internet or a smartphone. The highest number of tweets immediately issue statements were made. Content during the progressive tweets core keywords you do not often discussed, followed by the negative information increases the number of tweets has become a policy issue. Top retweets was to evaluate the process of debate, regardless of the issue. Tweeter complements the TV so Twitter has made public opinion. Smart phones and SNS Twitter, combined with the TV and the participation and direct democracy, voters vote one instrument was realized. Should forward approval ratings, real-time Twitter subtitles on the TV screen in TV debate Twitter influence in the election will be greatly expanded.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.787-793
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• 2022

After COVID-19, with the emergence of social distancing, non-face-to-face services, and home economics, visiting dining out is rapidly being replaced by non-face-to-face dining out. The purpose of this study is to find ways to create a safe dining culture centered on living quarantine in line with the changing trend of the restaurant industry after the outbreak of COVID-19, establish the direction of food culture improvement projects, and enhance the effectiveness of the project. This study used TEXTOM to collect and refine search frequency, perform TF-IDF analysis, and Ucinet6 programs to implement visualization using NetDraw from January 1, 2018 to October 31, 2019 and December 31, 2021, and identified the network between nodes of key keywords. Finally, clustering between them was performed through Concor analysis. As a result of the study, if you check the frequency of searches before and after COVID-19, it can be seen that the COVID-19 pandemic greatly affects the changes in the restaurant industry.

• The Journal of Korean Medicine
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• v.43 no.4
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• pp.102-130
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• 2022

Objectives: The persistence and unpredictability of coronavirus disease (COVID-19) and new measures to prevent direct medical intervention (e.g., social distancing and quarantine) have induced various psychological symptoms and disorders that require self-treatment approaches and integrative treatment interventions. To address these issues, the Korean Medicine Mental Health (KMMH) center developed a field manual by reviewing previous literature and preexisting manuals. Methods: The working group of the KMMH center conducted a keyword search in PubMed in June 2021 using "COVID-19" and "SARS-CoV-2". Review articles were examined using the following filters: "review," "systematic review," and "meta-analysis." We conducted a narrative review of the retrieved articles and extracted content relevant to previous manuals. We then created a treatment algorithm and recommendations by referring to the results of the review. Results: During the initial assessment, subjective symptom severity was measured using a numerical rating scale, and patients were classified as low- or moderate-high risk. Moderate-high-risk patients should be classified as having either a psychiatric emergency or significant psychiatric condition. The developed manual presents appropriate psychological support for each group based on the following dominant symptoms: tension, anxiety-dominant, anger-dominant, depression-dominant, and somatization. Conclusions: We identified the characteristics of mental health problems during the COVID-19 pandemic and developed a clinical mental health support manual in the field of Korean medicine. When symptoms meet the diagnostic criteria for a mental disorder, doctors of Korean medicine can treat the patients according to the manual for the corresponding disorder.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• Korean Journal of Social Welfare
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• v.67 no.2
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• pp.61-84
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• 2015

The purpose of this study was to develop the Post-bereavement Growth Inventory(PBGI) for the elderly and to evaluate its validity. The items for developing PBGI were compiled from a literature search, content validity review, and a preliminary survey. A main survey of 308 widowed persons(aged 60+) was conducted. The results were as follows. First, an exploratory factor analysis revealed six factors of the PBGI. The results of the confirmatory factor analysis supported a five-factor structure for the final 24 scale items of scale. Second, in terms of concurrent validity, the PGBI compared favorably with the "Personal Growth" scale from The Hogan Grief Reaction Checklist (HGRC: Hogan and Schimdt, 2001). Third, the results of contrast group validity tests demonstrated that the group scoring in the upper 30% on the scales of Psychological Well-being (PWB, Ryff and Keyes, 1995) and the Grief Reaction Inventory (GRI: Remondet and Hansson, 1987) showed a significantly higher degree of post-bereavement growth (PBG) than the group scoring in the lower 30%. Fourth, Cronbach's ${\alpha}$ values for the five factors were between .649-.856 and .907 overall, indicating high internal consistency. Finally, education had the most significant influence on PBG. All results indicated the high validity of the PBGI. The findings from this study could provide the rationale for practical interventions from a growth-oriented perspective for widowed persons, as well as an objective assessment tool for practitioners in clinical areas.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.109-135
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• 2021

his study classified the development process of artificial intelligence (AI) speakers through analysis of the news text of artificial intelligence (AI) speakers shown in traditional news reports, and identified the characteristics of each product by period. The theoretical background used in the analysis are news frames and topic frames. As analysis methods, topic modeling and semantic network analysis using the LDA method were used. The research method was a content analysis method. From 2014 to 2019, 2710 news related to AI speakers were first collected, and secondly, topic frames were analyzed using Nodexl algorithm. The result of this study is that, first, the trend of topic frames by AI speaker provider type was different according to the characteristics of the four operators (communication service provider, online platform, OS provider, and IT device manufacturer). Specifically, online platform operators (Google, Naver, Amazon, Kakao) appeared as a frame that uses AI speakers as'search or input devices'. On the other hand, telecommunications operators (SKT, KT) showed prominent frames for IPTV, which is the parent company's flagship business, and 'auxiliary device' of the telecommunication business. Furthermore, the frame of "personalization of products and voice service" was remarkable for OS operators (MS, Apple), and the frame for IT device manufacturers (Samsung) was "Internet of Things (IoT) Integrated Intelligence System". The econd, result id that the trend of the topic frame by AI speaker development period (by year) showed a tendency to develop around AI technology in the first phase (2014-2016), and in the second phase (2017-2018), the social relationship between AI technology and users It was related to interaction, and in the third phase (2019), there was a trend of shifting from AI technology-centered to user-centered. As a result of QAP analysis, it was found that news frames by business operator and development period in AI speaker development are socially constituted by determinants of media discourse. The implication of this study was that the evolution of AI speakers was found by the characteristics of the parent company and the process of co-evolution due to interactions between users by business operator and development period. The implications of this study are that the results of this study are important indicators for predicting the future prospects of AI speakers and presenting directions accordingly.

• Hwankyungkyoyuk
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• v.3 no.1
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• pp.141-152
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• 1992

The purpose of this study is to search for the ways to diversify Outdoor/Environmental Education Program in the Nature Study Center(NSC), especially for the middle school students. For this study, various research methods such as literature review, questionnaire survey (448 students, 11 middle school teachers, 19 NSC staffs), interview and participant observation are used the process of this study consists of two steps. The first step is to define research questions through pilot survey and the second is to investigate the research questions, in the form of hypotheses through main survey. Nine hypotheses are formulated. Six are related with program elements (educational goals, student characteristics, staff resources, teaching methods, instructional resources, contents). three are related with program implementation process(preplan, implementation, post-evaluation). The hypotheses are tested and alternatives for program improvement are proposed. 1. Educational goals : Educational goals of NSC should be focused on Outdoor /Environmental Education and each NSC should specialize on its own theme. The objectives of every sub-program should be unified toward educational goals. 2. Student characteristics: The Outdoor/Environmental Education Program should reflect student characteristics: sex, urban/rural origins, normal/handicapped, number of visit and so on. 3. Staff resources : Provide qualified staff with professional knowledge and positive attitudes, reeducate staffs periodically, reduce management staff and increase teaching staffs. Provide permanent and well paid position, encourage and give opportunities and the middle school teachers to participate in program. 4. teaching method: Increase outdoor classes two way communication between teaching staffs and students adopt more open ended teaching method so that students can exercise coworks in small groups. 5. Instructional resources: Diversify NSC sites(mountains, coastal areas, urban areas and so on), teaching media (audio/visual equipments, graphic design of signs). Consider design for handicapped and integrate indoor and outdoor educational facilities. Plan nature trails with separate themes, allign nature trail so that it passes through diverse environments. 6. Content : Reflect characteristic site potential specialize on day or night program, on seasonal program, and on site specific social issues(such as interpreting of environmental damages around the NSCs). 7. Preplan: Get Information and know about visiting students in advance. Discuss with middle school teachers and adjust program weeks before visits if many or all of the students had already visited a NSC. arrange a visit to other NSC. Provide an introductory class for the teachers and students before they visit a NSC. 8. Implementation: During NSC visit and classes apply various and appropriate techniques to collect in formation for later evaluation. Improve NSC provided evaluation sheet so as to reflect student characteristic. Compare with formal education and investigate on effects of NSC program. 9. Post-evaluation: Formalize a post-evaluation process and organization. During the winter vacation, develop new programs based on the post-evaluationacation, for the next year. Also, have comparative evaluation meetings of staff from various NSCs during the winter vacation while there is no visitors and classes.