• Asia pacific journal of information systems
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• 제24권2호
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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.

• 한국IT서비스학회지
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• 제16권3호
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• 경영정보학연구
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• 제17권3호
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• pp.183-202
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• 2015

최근 몇 년 동안 다양한 Social Network Service(SNS)가 소비자들 사이에서 많은 인기를 누리며 새로운 커뮤니케이션 수단으로 급부상 하였다. 이에 따라 SNS를 이용한 온라인 구전 마케팅이 성행하게 되었다. 이때, 대다수의 기업들이 SNS상에서 인기(팔로워 혹은 방문자수 기준)가 많을수록 영향력이 크다는 것을 전제로 온라인 구전 마케팅의 수단으로 쓰일 SNS를 선정한다. 또한 SNS상의 인기도 혹은 영향력에 대한 기존의 연구들은 인기도와 영향력을 구분하지 않고 혼용하여 사용하고 있다. 이에 본 연구에서는 SNS상의 인기와 영향력 간의 상관관계를 실증 분석해 보고, 실제로 인기와 영향력에 영향을 미치는 변수들을 도출하고자 하였다. SNS를 사용하는 비율이 높은 20대를 대상으로 설문조사를 실시하고, PLS 2.0을 이용해 분석하였다. 연구 결과 인기와 영향력은 약한 상관관계를 가지는 것으로 나타났다. 때문에 향후 연구에서는 인기와 영향력을 구분해야 할 필요가 있으며, 영향력이 있거나 인기가 있는 SNS를 판별하기 위한 각각의 새로운 기준이 필요하다는 것을 밝혀냈다.

• 아동학회지
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• 제17권1호
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• pp.259-273
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• 1996

The purpose of this study was to investigate the relationship between children's popularity and Interpersonal Cognitive Problem-Solving Skill(ICPS). The subjects were 162 children(70 popular, 76 rejected, and 16 neglected children) chosen from 359 children between the age of 5 -6 and 8-9 years of age. The materials were peer nomination measures developed by Moreno(1934 ) and Interpersonal Cognitive Problem-Solving Skill produced by Park, Chan-Ok from IPCS of Spivack(1976). The data were analyzed by 3-way ANOVA popularity (3) ${\times}$ age (2) ${\times}$ sex (2), t-test, and $Scheff\acute{e}$ test. The results were that (1) children's popularity was significantly different by sex, (2) children's ICPS was significantly different by age for boys, (3) there was no significant difference in ICPS by popularity, and (4) there were significant differences in positive negative solution thought.

• 대한가정학회지
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• 제43권8호
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• pp.13-23
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• 2005

The purpose of this study was to examine the relationship between mind understanding, popularity, and friendship of preschool children. A total 1444-and 5-year old children participated in this study. The children were assessed on false belief, emotion understanding, language skill, and popularity in peer groups. Their teachers rated the children's friendship qualities. Significant differences in mind understanding based on social status and friendship status were found. Popularity, number of mutual friend, PPVT, and positive interaction between friends were found to be significant predictors of children's mind understanding.

• 한국콘텐츠학회논문지
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• 제18권6호
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• pp.706-718
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• 2018

본 연구는 네이버TV의 이용자들이 네이버TV에서 제공하는 영상클립을 어떻게 이용하는지 살펴보고, 영상클립의 인기도에 영향을 미치는 요인들을 실증적으로 분석했다. 이를 위해 2017년 9월 10일부터 9월 24일까지 2주간 네이버TV의 영상클립 상위50위에 올랐던 영상클립 572개를 선정해 분석대상으로 삼았다. 분석대상 영상의 성격은 장르, 유형, 스타출연 여부로 나눴고, 성격에 따른 인기 정도를 알아보기 위해 개별 영상클립 인기도를 지수화 했다. 연구결과, 이용자 반응특성 중에서는 개별 영상클립의 좋아요수, 프로그램 채널 구독자수가 영상클립 인기정도와 정적 관련성이 있는 것으로 나타났다. 영상클립 특성 중에서는 좋아요수, 프로그램 채널 구독자수, 장르, 유형, 스타출연 여부가 영상클립 인기에 영향을 미치는 요인이었다. 장르 중 기타장르의 영향력 정도가 가장 낮았으며, 미세한 차이지만 드라마, 음악, 예능장르 순으로 영향력 정도가 높았다. 유형 중 웹 전용, 미방송분 유형이 하이라이트 유형의 영상클립에 비해 통계적으로 유의한 수준으로 영상클립의 인기정도에 영향을 미치는 것으로 나타났다. 마지막으로 영상클립 내에 스타가 등장할 경우 영상클립의 인기정도가 더 높았다.

• 디지털콘텐츠학회 논문지
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• 제13권3호
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• pp.325-334
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• 2012

드라마 프로그램 제작은 창작 영역에 속하는 것으로 간주되어 왔다. 따라서 드라마 프로그램의 품질 향상에 대한 시스템적 접근은 별로 시도되지 않았다. 본 연구의 목적은 KBS에서 제작되는 드라마 프로그램의 시청률을 방영 이전에 예측할 수 있는 통계적 계산모델을 제시하는 데 있다. 이를 위해 시청률에 영향을 미치는 요인들을 찾아내고 이들의 상호관계를 회귀분석 기법으로 밝혀내어 시청률 예측모델을 도출했다. 본 연구결과는 드라마 프로그램의 제작에 필요한 각 투입 요소들의 적정 규모를 산정하는 데 유용하게 활용될 수 있다.

• International Journal of Contents
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• 제3권1호
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• pp.14-18
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• 2007

General replacement policy includes document-based LRU or LFU technique and other various replacement policies are used to replace the documents within cache effectively. But, these replacement policies function only with regard to the time and frequency of document request, not considering the popularity of each web site. In this paper, we present the document replacement policies with regard to the popularity of each web site, which are suitable for modern network environments to enhance the hit-ratio and efficiently manage the contents of cache by effectively replacing documents on intermittent requests by new ones.

• 아동학회지
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• 제29권3호
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• pp.191-205
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• 2008

This study explored gender differences in social status, acceptance/rejection, perceived popularity, social emotions, avoidance and anxiety by participant roles in bullying situations. Subjects were 215 6th grade children. Instruments were the Participant Roles (Sutton & Smith, 1999), Peer Nomination (Coie & Dodge, 1983 Cillessen & Mayeux, 2004), Social Avoidance and Social Anxiety (Franke & Hymel, 1984) scales. Results showed that more boys than girls were in pro-bullying participant role groups; more girls than boys were in outsider groups. Boy pro-bullies were high in social rejection. Boy defenders were high in popularity and low in social avoidance. Boy outsiders had high social anxiety. Girl victims had low social status, low social acceptance and lowest perceived popularity; they were high in social avoidance and social rejection.

• Management Science and Financial Engineering
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• 제11권3호
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• pp.137-153
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• 2005

Collaborative filtering-based recommendation systems make personalized recommendations based on users' ratings on products. Recommender systems must collect sufficient rating information from users to provide relevant recommendations because less user rating information results in poorer performance of recommender systems. To learn about new users, recommendation systems must first present users with an initial item list. In this study, we designed and analyzed seven selection strategies including the popularity, favorite, clustering, genre, and entropy methods. We investigated how these strategies performed using MovieLens, a public dataset. While the favorite and popularity methods tended to produce the highest average score and greatest average number of ratings, respectively, a hybrid of both favorite and popularity methods or a hybrid of demographic, favorite, and popularity methods also performed within acceptable ranges for both rating scores and numbers of ratings.