• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.551-561
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• 2016

A user influence discrimination scheme using big data from social networks is needed. In this thesis, we propose a user influence discrimination scheme considering reliability in social networks. The proposed scheme measures reliability scores through social activities and simplifies a social network by collecting only reliable users. It also derives user influence by considering direct and indirect influences that depends on network degree between users. As a result, the proposed scheme improves the expandability of the user influence. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluations in terms of reliability and user influence.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.35-36
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• 2016

소셜 네트워크가 발전함에 따라 인맥형성과 정보공유 의 목적뿐만 아니라 마케팅, 추천 등 여러 분야에 활용됨에 따라 정보를 단시간 내에 더 많은 사용자들이 공유하기 위해 영향력이 높은 사용자를 필요로 하고 이를 위해 정확한 사용자 영향력 판별이 요구된다. 그러나 누구나 정보를 공유할 수 있고 그 양이 너무 방대하기 때문에 신뢰성 없는 정보를 공유하는 문제가 발생한다. 이에 본 논문에서는 소셜 네트워크에서 신뢰성을 고려한 사용자 영향력 판별 기법을 제안한다. 제안하는 기법에서는 신뢰성에 의한 네트워크 간소화와 사용자의 직-간접적인 영향력을 고려하여 영향력을 판별함으로써 정확성을 향상시킨다.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.46-54
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• 2022

The existing methods for determining influence in social networks do not accurately determine the influence of users because they do not delete or update existing relationships before they stop in the face of an increasing number of inactive users on social networks. In this paper, we propose a user influence detremination method using the temporal k-shell decomposition technique based on the creation date of users of social networks. To solve the problem of increasing influence of old users in social networks, we apply k-shell decomposition and age-specific order centrality as attenuation coefficients due to aging in neighbors. The age-decaying k-shell decomposition and age-specific order centrality are searched for influential users at the present time by applying the attenuation coefficient and age-dependent weights. Various performance evaluations are performed to show the superiority of the proposed method.

• Communications of Mathematical Education
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• v.37 no.1
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• pp.21-45
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• 2023

This study presents a machine learning program designed to identify impactful papers in the field of mathematics education. To achieve this objective, we examined the impact of papers from a scientific econometrics perspective, developed a mathematics education research network, and defined the impact of mathematics education research using PageRank, a network centrality index. We developed a machine learning model to determine the impact of mathematics education research and identified the journals with the highest percentage of impactful articles to be the Journal for Research in Mathematics Education (25.66%), Educational Studies in Mathematics (22.12%), Zentralblatt für Didaktik der Mathematik (8.46%), Journal of Mathematics Teacher Education (5.8%), and Journal of Mathematical Behaviour (5.51%). The results of the machine learning program were similar to the findings of previous studies that were read and evaluated qualitatively by experts in mathematics education. Significantly, the AI-assisted impact evaluation of mathematics education research, which typically requires significant human resources and time, was carried out efficiently in this study.

• Korean journal of communication and information
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• v.49
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• pp.20-38
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• 2010

This paper was attempting to identify determinants of media repertoires based on new services and technologies. Using the regression and discriminant models, this study examined determinants that included five independent factors such as the degree of innovation, social networks, social influences, demographic variables and media uses. The analyses revealed that all of independent variables except the degree of innovation were significant determinants of media repertoires. Secondly, the results of discriminant analyses showed that terrestrial television use, age, disposable income were significant factors discriminating new media service adopters from the sample. For new media related technologies adopters, family income, and media uses such as newspaper, Internet and radio were significant discriminant variables.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.280-291
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• 2018

Recently, various interactions have been actively conducted through sharing and expressing opinions among users in social networks. In this process, since malicious users and fault information spread misinformation, trust is reduced irrespective of their will. To solve this problem, studies have been conducted to determine the trust of a user through direct-connected users. In this paper, we propose a enhanced user indirect trust discrimination scheme considering the connection relation and influence of users. The proposed indirect trust computation scheme derives the user's area of interest through user interaction and reconstructs the existing network considering the user connection relationship. The final indirect trust is also detected by determining whether the user is a malicious user through the influence of the user. Through various performance evaluations, we show that the proposed scheme achieves better performance than the existing method.

• 전기의세계
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• v.13 no.4
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• pp.1-9
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• 1964

수차발전기에서 이상진동이 발생하였을 경우 일반적인 진동판별법을 서술하기로 한다. 실제로 부디치는 진동문제를 취급하여 보면 단순한 원인에 의한 것 뿐만아니라 여러가지 원인이 중첩되어 복잡한 비선형적인 현상이 나타날 경우가 많다. 원동기측 및 피동기측의 회전자급 고정자의 각각에 진동을 발생하는 다수의 요인이 있고 또 각각의 진동원인들의 상호간의 영향력이 작용하여 복잡한 진동현상을 가지고 있어서 그 원인을 판별하기가 용이한 일이 아니다. 이러한 진동원인중 주 되는 몇가지 원인을 찾아내기 위하여는 진동특성에 관한 충분한 이해와 적절한 측정장치를 선택할 필요가 있다.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Information Systems Review
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• v.17 no.3
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• pp.183-202
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• 2015

In recent years, various Social Network Service (SNS) is emerging as a new means of communication were enjoying a lot of popularity among consumers. Accordingly, an online word-of-mouth marketing through the SNS is prevalent. At this moment, the majority of companies selects the SNS used as resources of online word-of-mouth marketing on the assumption that the more a SNS is popular (followers or visitors based), the more it has an influence. In addition, the existing studies about the popularity or influence on the SNS were not distinguish them separately. The former researchers used popularity mixed with Influence. Therefore, this study, we have conducted a survey with people in their twentieswho use SNS most to do an empirical analysis of the relationship between popularity and Influence on the SNS. According to the results of this study, it has a weak correlation between popularity and Influence. So, it is necessary to distinguish between popularity and influence.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.113-115
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• 2013

다수의 상품평 요약은 인터넷 쇼핑몰 고객에게 편의를 제공할 수 있다. 본 논문에서는 상품평 요약 시스템의 성능 향상을 위한 방안을 제안한다. 시스템은 크게 상품평의 평가 항목 추출과 극성 사전 생성, 극성 판별 단계로 구성된다. 평가 항목 추출에서는 외부 연관도의 영향력을 줄이고, 극성 사전 생성에서는 단어 거리 평균을 적용한다. 제안한 방식을 사용하였을 때 평가 항목에 대한 문장의 극성 판별 시 90.8%의 정확율을 보였다.