• Title/Summary/Keyword: media influence

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Analysis of Regional Fertility Gap Factors Using Explainable Artificial Intelligence (설명 가능한 인공지능을 이용한 지역별 출산율 차이 요인 분석)

  • Dongwoo Lee;Mi Kyung Kim;Jungyoon Yoon;Dongwon Ryu;Jae Wook Song
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.47 no.1
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    • pp.41-50
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    • 2024
  • Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

The Effects of Content and Distribution of Recommended Items on User Satisfaction: Focus on YouTube

  • Janghun Jeong;Kwonsang Sohn;Ohbyung Kwon
    • Asia pacific journal of information systems
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    • v.29 no.4
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    • pp.856-874
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    • 2019
  • The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

A Study on Recommendation Application of Air Purification Companion Plant using MBTI (MBTI를 통한 공기 정화 반려식물 추천 애플리케이션 연구)

  • Yu-Jun Kang;Youn-Seo Lee;Hyeon-Ah Kim;Hee-Soo Kim;Won-Whoi Huh
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.3
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    • pp.139-145
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    • 2024
  • Since COVID-19, most of people's main living spaces have been moved indoors. Due to this influence, many people's interest in companion plants continues to rise. People who raise companion plants often raise them for the purpose of emotional stability or air purification. In fact, plants have the effect of giving people a sense of emotional stability and the ability to purify indoor air is excellent depending on what kind of plant they are. However, if you do not have knowledge of plants, you will not know which plants have excellent air purification effects, and even if you grow them, you will face a problem that withers quickly. Therefore, in this paper, we develop an app that provides users who do not have prior knowledge to store and manage their MBTI and member information in a database using databases and MBTI, and based on this, recommend plant data that fits their preferences with the user and manage their schedules through calendars.

Analysis of Z Generation's Collaborative Information Activities through Challenges: Focusing on Korean College Students (챌린지에 나타난 Z세대의 협업 정보 활동 - 국내 대학생을 중심으로 -)

  • Ji Hei Kang
    • Journal of Korean Library and Information Science Society
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    • v.55 no.1
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    • pp.173-192
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    • 2024
  • Through a survey of college students, this study explained the process and aspects of how Generation Z interacted with information while collaborating on a daily basis. Applying the Radical Change Theory, the study investigated the platforms through which university students engaged in challenges and their information behaviors. University students primarily participated in challenges using platforms such as Instagram and Naver Blog. In terms of changes in information seeking, which is the first type of information seeking in the theory, a wide and diverse range of information behaviors were observed, with the way of searching for information being quite different from when the digital age arrived 10 years ago. Information sources included not only digitalized ones but also traditional sources such as printed materials and personal contacts. The utilization of various media types was prominent, and collaborative efforts were voluntarily undertaken for challenges. In terms of changing perspectives, type 2 of information behavior, the main motivation for acquiring information and securing knowledge led to participation in the challenge. Participants exhibited a sense of community consciousness, including mutual influence awareness, social participation consciousness, and emotional connection.

Research on Music Application UI Design and Feature Preferences by MBTI Personality Types (MBTI 성격 유형별 음악 애플리케이션 UI 디자인 및 기능 요구 선호도 연구)

  • Wu Yuhang;Inyong Nam;Bao Wenhua
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.3
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    • pp.437-449
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    • 2024
  • This study analyzed the influence of MBTI (Myers-Briggs Type Indicator) personality types on preferences for user interface (UI) design in music applications. Through an online survey, 535 responses were collected, and data were processed using ANOVA analysis in Python. The analysis revealed that certain MBTI types tend to prefer combinations of warm and neutral color tones, aligning with their artistic sensibilities and emphasis on harmony. Conversely, other MBTI types show a preference for colder color tones or combinations of cold and neutral tones, reflecting their practical and systematic tendencies. Additionally, it was found that UI layout preferences also vary according to personality types. Some MBTI types exhibit a preference for the 'Mostly Fluid' model, reflecting their efficient and systematic nature. These findings underscore the importance of considering users' individual personality types in UI design for music applications.

Effect of Virtual Influencer Attributes on Consumer Purchase Intentions : Evidence from Chinese Internet Consumers (가상 인플루언서 특성이 소비자 구매의도에 미치는 영향 : 중국 인터넷 소비자를 대상으로 한 실증연구)

  • Niu, Yanqi;Song, Hyo-jung;Kim, Tae-ha
    • Journal of Venture Innovation
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    • v.7 no.2
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    • pp.57-76
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    • 2024
  • A virtual influencer is a digital artifact that primarily operates on social media and acts like the human influencer. This study investigate how the attractiveness and reputation of virtual influencers affect consumers' purchase intentions. Based on previous studies, we developed a research model and questionnaire, and analyzed a total of 371 survey questionnaires using a structural equation model. The study results show that reputation positively affect attachment, imitation desire, and interaction, while attractiveness positively influence attachment and imitation desire but not interaction. Lastly, purchase intention was positively influenced by imitation desire, attachment, and interaction; which indicates that attractiveness, reputation, and interactivity are crucial factors in virtual influencer implementation strategies. The quality and reliability of contents provided by virtual influencers are essential not only for gaining popularity but also for maintaining a good reputation. Additionally, continuous interaction is necessary in order to foster close relationships with consumers. A few practical insights are presented for firms considering to implement a virtual influencer.

Incidence of narcolepsy symptoms after taking COVID-19 vaccines: a Jordanian cross-sectional study

  • Mohammad Al Katatbeh;Yazan Al-Mashakbeh;Hadeel Freihat;Hiba Gharam;Rahmeh Mohammad;Rahma Aldalki;Sadeen Eid;Reema Sharman;Nizar Heissat;Ghusoon Al-Samarraie;Ahmad Al-Shaibie;Laith Khasawneh
    • Clinical and Experimental Vaccine Research
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    • v.13 no.3
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    • pp.218-224
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    • 2024
  • Purpose: Sleeping disorders were reported in many patients who took vaccines during previous pandemics. We aim to investigate the relationship between coronavirus disease 2019 (COVID-19) vaccines and the incidence of narcolepsy symptoms in the Jordanian population. Materials and Methods: We used a descriptive, cross-sectional, online self-administered survey conducted between December 2022 and May 2023. The survey targeted males and females above the age of 18 years who took any type of COVID-19 vaccine, had no chronic diseases, and had no sleep disorders prior to taking the vaccine. The survey was distributed via social media platforms. Results: A total of 873 participants were included in this study, consisting of 44.4% males and 55.6% females, with the majority being in the 18-29 age group. Most participants (79.8%) received two vaccine doses, with the Pfizer vaccine being the most common. Nearly half of the participants reported excessive daytime sleepiness. Sleep paralysis and hypnagogic hallucinations were reported by a notable proportion of participants, but no significant differences were found among the vaccine types. Sleep attacks and fragmented nighttime sleep were associated with the number of vaccine doses received, suggesting a possible influence of the dose count on these symptoms. The presence of excessive daytime sleepiness, sudden loss of muscle tone, sleep paralysis, and hypnagogic hallucinations showed no significant association with the number of doses taken. Conclusion: We hypothesize a possible link between COVID-19 vaccination and the emergence of narcolepsy symptoms in Jordanian individuals. Additional investigations and continuous monitoring to determine the extent of the risk and uncover potential mechanisms behind this connection should be performed.

A study on modeling of boiling heat transfer in core debris bed of SFR

  • Venkateswarlu S.;Hemanth Rao E.;Prasad Reddy G.V.;Sanjay Kumar Das;Ponraju D.;Venkatraman B.
    • Nuclear Engineering and Technology
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    • v.56 no.9
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    • pp.3864-3871
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    • 2024
  • In case of a hypothetical severe accident in a Sodium-cooled Fast Reactor (SFR), coolability of the debris bed in the post-accident phase plays a vital role in mitigating the accident and ensuring the structural integrity of the reactor vessel. Few numerical studies are reported in literature, in which the boiling heat transfer in debris bed is expressed as equivalent heat conduction using similarity law between heat conduction and two-phase heat transfer. However, these studies assumed steady state mass conservation for the boiling zone and neglected the gravity force. Hence, a detailed study has been carried out for various particle sizes and porosities of SFR debris to investigate the influence of above considerations. The effect of gravity on debris bed coolability is studied using steady state model of Lipinski, which showed that gravity has a non-negligible effect, for particle size of 0.3 mm and porosity of 0.5. However, the gravitation force was found to have a negligible effect in dryout heat flux estimation for the bottom cooled configuration. A transient numerical model is developed for simulating the boiling phenomena in debris beds and validated with the published experimental results. The assumption of steady state mass conservation is verified by carrying out transient analysis, which indicated early prediction of the dryout inception. For time dependent heat generation case, the unsteady mass conservation predicted higher DHF compared to constant heat generation.

An Analytical Approach Using Topic Mining for Improving the Service Quality of Hotels (호텔 산업의 서비스 품질 향상을 위한 토픽 마이닝 기반 분석 방법)

  • Moon, Hyun Sil;Sung, David;Kim, Jae Kyeong
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.21-41
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    • 2019
  • Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

Measuring the Third-Person Effects of Public Opinion Polls: Focusing On Online Polls (여론조사보도에 대한 제3자효과 검증: 온라인 여론조사를 주목하며)

  • Kim, Sung-Tae;Willnat, Las;Weaver, David
    • Korean journal of communication and information
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    • v.32
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    • pp.49-73
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    • 2006
  • During the past decades, public opinion polls have become an ubiquitous tool for probing the complexity of people's beliefs and attitudes on a wide variety of issues. Especially since the late 1970s, the use of polls by news organizations has increased dramatically. Along with the proliferation of traditional polls, in the past few years pollsters and news organizations have come to recognize the advantages of online polls. Increasingly there has been more effort to take the pulse of the public through the Internet. With the Internet's rapid growth during the past years, advocates of online polling often emphasize the relative advantages over traditional polls. Researchers from Harris Black International Ltd., for example, argue that "Internet polling is less expensive and faster and offers higher response rates than telephone surveys." Moreover, since many of the newer online polls draw respondents from large databases of registered Internet users, results of online polls have become more balanced. A series of Harris Black online polls conducted during the 1998 gubernatorial and senatorial elections, for example, has accurately projected the winners in 21 of the 22 races it tracked. Many researchers, however, severely criticize online polls for not being representative of the larger population. Despite the often enormous number of participants, Internet users who participate in online polls tend to be younger, better educated and more affluent than the general population. As Traugott pointed out, the people polled in Internet surveys are a "self selected" group, and thus "have volunteered to be part of the test sample, which could mean they are more comfortable with technology, more informed about news and events ... than Americans who aren't online." The fact that users of online polls are self selected and demographically very different from Americans who have no access to the Internet is likely to influence the estimates of what the majority of people think about social or political issues. One of the goals of this study is therefore to analyze whether people perceive traditional and online public opinion polls differently. While most people might not differentiate sufficiently between traditional random sample polls and non representative online polls, some audiences might perceive online polls as more useful and representative. Since most online polls allow some form of direct participation, mostly in the form of an instant vote by mouse click, and often present their findings based on huge numbers of respondents, consumers of these polls might perceive them as more accurate, representative or reliable than traditional random sample polls. If that is true, perceptions of public opinion in society could be significantly distorted for those who rely on or participate in online polls. In addition to investigating how people perceive random sample and online polls, this study focuses on the perceived impact of public opinion polls. Similar to these past studies, which focused on how public opinion polls can influence the perception of mass opinion, this study will analyze how people perceive the effects of polls on themselves and other people. This interest springs from prior studies of the "third person effect," which have found that people often tend to perceive that persuasive communications exert a stronger influence on others than on themselves. While most studies concerned with the political effects of public opinion polls show that exit polls and early reporting of election returns have only weak or no effects on the outcome of election campaigns, some empirical findings suggest that exposure to polls can move people's opinions both toward and away from perceived majority opinion. Thus, if people indeed believe that polls influence others more than themselves, perceptions of majority opinion could be significantly altered because people might anticipate that others will react more strongly to poll results.

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