• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.47-50
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• 2017

PC 혹은 모바일 환경이 점점 발전됨에 따라 사용자들은 자신의 목적에 맞는 다양한 콘텐츠들을 쉽게 이용하고 있다. 사용자는 정상적인 콘텐츠를 이용할 경우 콘텐츠가 가지고 있는 태그정보를 통해 콘텐츠의 내용을 식별하여 사용한다. 하지만 올바른 태그정보가 들어있지 않은 콘텐츠의 경우 사용자는 잘못 입력된 태그정보로 인해 콘텐츠를 식별하는 과정에서 어려움을 겪을 수 있는 문제점을 가지고 있다. 본 논문에서는 사용자가 가지고 있는 콘텐츠에 접근하여 스마트폰 혹은 PC에 저장되어있는 다양한 콘텐츠들을 유튜브 API를 통해 유튜브 플랫폼에 존재하는 영상들과 매칭 시키고, 매칭 시킨 영상들을 사용자 계정에 동기화시키는 시스템을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.53-60
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• 2022

YouTube realistically shows fake news and biased content based on facts that have not been verified due to low entry barriers and ambiguity in video regulation standards. Therefore, this study aims to analyze the influence of the media and YouTube on individual behavior and their relationship. Data from YouTube and Twitter are randomly imported with selenium, beautiful soup, and Twitter APIs to classify the 31 most frequently mentioned keywords. Based on 31 keywords classified, data were collected from YouTube, Twitter, and Naver News, and positive, negative, and neutral emotions were classified and quantified with NLTK's Natural Language Toolkit (NLTK) Vader model and used as analysis data. As a result of analyzing the correlation of data, it was confirmed that the higher the negative value of news, the more positive content on YouTube, and the positive index of YouTube content is proportional to the positive and negative values on Twitter. As a result of this study, YouTube is not consistent with the emotion index shown in the news due to its secondary processing and affected characteristics. In other words, processed YouTube content intuitively affects Twitter's positive and negative figures, which are channels of communication. The results of this study analyzed that YouTube plays a role in assisting individual discrimination in the current situation where accurate judgment of information has become difficult due to the emergence of yellow media that stimulates people's interests and instincts.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.169-186
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• 2020

This study aims to examine the characteristics of news content and users' reactions in local broadcasting Youtube news' channel, and explore how the local media should response in the new online video environment. YouTube Open API sampled 3,950 news-related videos uploaded over a month on 31 YouTube news channels nationwide. The content analysis was performed on the basis of the analysis of individual videos, such as characteristics of each content and users' reactions. As a result, a few news channels have produced digital-only content, but the ratio has been very low, most were broadcast replay videos with titles and formats uploaded as they were. In some cases, it still operates as a comprehensive channel, which failed to show its expertise as an independent digital news platform. This shows that theses YouTube channels lacks differentiation from TV or its own web page, and is still skewed to the auxiliary role or online archive function of TV platform. Nevertheless, digital-only content, which can be a national issue based on regional expertise, has led to a higher number of views and users reactions, suggesting that is a realistic and effective strategy with expandability in online space in the future.

• Korean Association of Arts Management
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• no.57
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• pp.59-84
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• 2021

This study examined the structure of YouTube video network and the factors for the diffusion of Chinese creator's videos through the case of famous Chinese creator Fengtimo. There is few interest to the diffusion of Chinese contents among Korean researchers, while they have been studied the consumption of Hallyu(Korean wave) contents overseas. Using the data that YouTube Data API offers, this study analysed the video network that the comments of which are same users with NodeXL tools and the regression model with JASP tools. The study found that there are three groups of the YouTube channels of that network. They are domestic official accounts of Fengtimo, foreign officail accounts of Fengtimo and individual creators' accounts. The official accounts share the videos of Fengtimo's songs and entertainment contents for the fans, where the individual creators share their own meme videos(UGC). The significant factors for the diffusion in the YouTube video network are comments, likes, out-degree, dislikes, in-degree and betweenness centrality. There are significant difference between official channel and indivisual groups on the views. And degree and betweenness centrality have mediating effect. It is necessary to conduct more research on that subject with many other cases if we want to get to know the generalized explanation.

• Journal of Internet of Things and Convergence
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• v.4 no.1
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• pp.31-36
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• 2018

In this paper, we developed a chatbot system using the Django framework using the KakaoTalk API so that college students can easily search for important information in their university. Unlike existing chatbot systems that provide only specific information, the chatbot developed in this research automatically provides search results for various types of user queries such as weather, YouTube, Naver real-time ranking search and language translation as well as important information within their own university. We developed a module using Apache, Python and Django in AWS Ubuntu server and developed a chatbot system that automatically responds to user queries by communicating with KakaoTalk server using KakaoTalk API and BeautifulSoup. The system developed in this study is expected to be applicable to the future university entrance information promotion and election promotion system.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.102-108
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• 2019

Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.

• The Journal of Information Systems
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• v.32 no.2
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• pp.177-190
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• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.49-55
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• 2023

In this study, models for predicting the popularity of mukbang content on YouTube were proposed, and factors influencing the popularity of mukbang content were identified through post-analysis. To accomplish this, information on 22,223 pieces of content was collected from top mukbang channels in terms of subscribers using APIs and Pretty Scale. Machine learning algorithms such as Random Forest, XGBoost, and LGBM were used to build models for predicting views and likes. The results of SHAP analysis showed that subscriber count had the most significant impact on view prediction models, while the attractiveness of a creator emerged as the most important variable in the likes prediction model. This confirmed that the precursor factors for content views and likes reactions differ. This study holds academic significance in analyzing a large amount of online content and conducting empirical analysis. It also has practical significance as it informs mukbang creators about viewer content consumption trends and provides guidance for producing high-quality, marketable content.

• Journal of Digital Contents Society
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• v.17 no.5
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• pp.307-316
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• 2016

A user usually gets the same suggesting results as everyone else in most of the multimedia social services, nowadays. To address the challenging problem of personalization in the social network, we propose a method which exploits user's activities, user's moods, and user's friend relationships from the social network to build a decision-making system. Depending on a current state of the user's mood, this system infers the most appropriated video for the user. In the system, the user evaluates a set of the given recommendation methods which extract from the user's database social network and assigns a vague value to each method by a weight. Then, we find the fuzzy collection solution for the system and classify the set of methods into subsets, and order the subsets based on its local dominance to choose the best appropriate method. Finally, we conduct an experiment using the YouTube API with a lot of video types. The experiment result shows that the channel recommendation system appropriately affords the user's character, it is more satisfying than the current YouTube based on an evaluation of several users.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.61-74
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• 2016

As e-Sports evolves, a variety of industries grow together. Game broadcastings contribute to spread video game culture through various media platforms. Hence people start to perceive e-sports as a genre of sports and the demand on real-time game broadcasting increases. The global game broadcasting channels based on OTT(Over-The-Top) service also increase rapidly. In this paper we understand the status of streaming service and watching attitude of global game broadcasting. We also provide practical suggestions along with analysis results.