• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.467-474
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• 2024

This study analyses customer data from a US online lending platform to empirically document the discriminatory treatment that low- and middle-income borrowers face in financial markets. Researchers are using financial data from nearly 2.93 million loans between 2007~2020 of the Lending Club on the open-source Kaggle platform. We find that thin-filers borrowers, especially those with lower credit scores, receive loans at higher interest rates. This discriminatory treatment undermines financial inclusion and has the potential to increase social inequality. The significance of this research is that it sheds substantial light on the problem of inequality in financial markets and, based on the findings, suggests concrete measures to ensure equitable access to finance for all customers and enhance sustainable financial inclusion. In doing so, we propose a shift towards enhancing the social responsibility of institutions.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.75-86
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• 2021

Brand image refers to how customers, stakeholders and the market see and recognize the brand. A positive brand image leads to continuous purchases, but a negative brand image is directly linked to consumers' buying behavior, such as stopping purchases, so from the corporate perspective, it needs to be quickly and accurately identified. Currently, methods of investigating brand images include surveys and SNS surveys, which have limited number of samples and are time-consuming and costly. Therefore, in this study, we are going to conduct an emotional analysis of text data on social media by utilizing the machine learning based KoBERT model, and then suggest how to use it for game corporate brand image analysis and verify its performance. The result has proved some degree of usability showing the same ranking within five brands when compared with the BRI Korea's brand reputation ranking.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.391-394
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• 2009

정보의 양이 폭발적으로 증가함에 따라 웹 사용자가 원하는 적합한 데이터를 찾아내는 것은 매우 어렵다. 이는 웹 사용자마다 서로 다른 검색의도와 질의의 모호성에 의한 것으로, 이와 같은 검색의 어려움을 해결하기 위해 많은 연구들이 수행되어 왔다. 질의 로그는 검색자의 검색 의도가 내포되어 있는 중요한 자료이다. 따라서 웹 사용자별 질의 로그 패턴을 분석하여 유사한 질의를 사용하는 웹 사용자들을 클러스터링 하여 검색에 적용한다면 좀 더 유용한 정보를 획득할 수 있다. 즉, 특정 카테고리와 연관된 질의를 자주 사용하는 웹 사용자들은 해당 분야에 관심이 많을 것이며, 또한 다른 카테고리에 관심이 높은 사람보다 상호간에 소셜지수가 높게 나타날 것이다. 특정 주제에 대해 검색을 할 경우 해당 분야에 관심이 높은 웹 사용자들의 질의 및 클릭한 URL 정보를 상속받을 수 있다면 찾고자 하는 정보에 보다 빨리 접근할 수 있다. 따라서 본 연구는 질의패턴 분석을 통해 카테고리별로 관심도가 높은 웹 사용자들을 클러스터링 한 후 해당 카테고리에 대한 정보 검색시 이들이 사용한 질의와 클릭한 URL 정보를 웹 사용자들에게 제공해줌으로써 정보검색의 편의성을 향상시키기 위한 방안을 제안한다.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.88-106
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• 2022

This study examined the social discourse on consumer boycott and explored consumer experience using text mining of mass media and social media data and the in-depth interview. The result showed that the topics of online news related to the boycott included the causes of the boycott, the responses of each actor in the process of the boycott, and the effects of the boycott. In the result of the in-depth interviews, it was found that the boycott has been decentralized and the participants had the experience of exploring and verifying information on their own. In the boycott process, there were mixed experiences due to the absence of substitutes and the marketing influence, and positive experiences of expressing one's thoughts and strengthening beliefs through the boycott.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.310-310
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• 2022

최근 통신의 발달로 인하여 웹(Web)상에는 다양한 데이터들이 실시간으로 생산되고 있으며 해당 내용은 다양한 산업에서 활용되고 있다. 특히 최근에는 재난과 관련 상황에서도 소셜 네트워크 서비스(SNS) 데이터가 활용되기도 하며 기존의 수치 계측 데이터가 아닌 하나의 센서 역할을 하는 개인의 비정형데이터의 업로드가 다양한 재난 모니터링 부분에 활용되고 있는 실정이다. 특히 홍수 등의 자연재해 발생 시 개개인의 업로드 한 웹 데이터에는 시간에 따른 인구의 유동성이나 간단한 위치 정보 등을 포함하여 실제 피해의 정도를 보다 빠르고 다양한 정보로 모니터링이 가능하다. 홍수 발생 시 일반적으로 활용하는 수문 데이터는 피해의 규모가 크게 예측되는 대하천 위주로 관측이 이루어지며 관측지역과 데이터의 양이 한정되어있어 비정형데이터를 함께 활용한 연구가 필요하다. 따라서 본 연구에서는 웹에 있는 비정형 데이터들을 추출해내는 웹 크롤러를 구성하고 해당 프로그램을 활용하여 추출한 데이터들에 대해 강우 사상과 공간적 패턴을 비교 분석하여 크라우드 소싱 데이터를 적용한 홍수 피해지도의 활용방안을 제시하고자 한다.

• The KIPS Transactions:PartC
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• v.18C no.4
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• pp.217-226
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• 2011

As a diverse range of smartphones has been recently developed and diffused, the users of SNS (Social Network Service) also have been sharply increased. The SNS saves a variety of information such as exchanged pictures and videos, voice mails or location sharing, chat history, etc. as well as simple user data, so that the acquisition of data that are useful in the aspect of digital forensic is achievable. This thesis reviews the types of SNS that are available for the iPhone, a recent example of highly used smartphones, and types of data by each client. Also, efficient data analysis method for digital forensic investigations is suggested by analyzing the relationships within the collected data by each client.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.815-821
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• 2021

The advantage of big data is to collect a large amount of data on the Internet and refine and use valuable data. That is, the unstructured data is processed so that the user can analyze and utilize it from a necessary point of view. This paper is a relatively small project and is based on unstructured data that can be closely applied to real life and used for marketing. The subjects of the experiment were modeled on lodging companies in the Seoul metropolitan area an hour away from Seoul, and analyzed for the increase in lodging rates before and after marketing using big data. As an experiment that shows the effects of increasing sales, reducing costs, and increasing returns by users, we propose a system to determine and filter whether data input in the process of analyzing big data such as social networks can be used as accommodation-related information.

• 한국노년학
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• v.41 no.4
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• pp.505-525
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• 2021

Purpose. The purpose of this study is to examine how the phrase "old person" are expressed and used in the online sphere. Based on the theoretical concept of stigma, this study investigates the images and attitudes in society toward the elderly, and the characteristics of hate speech aimed at the elderly. Method. This study conducted text mining based on social big data using anonymous conversations. Results. It was confirmed that the elderly images shared online were generally negative. The attitudes expressed toward them also tended to be negative due to the negative images that are propagated of the elderly. The hate speech relating to the elderly, in usages such as 'Teul-ttag' and 'Kon-dae', were mainly identified in comments that negatively evaluate the elderly, and these expressions demonstrate the depth of hate and discrimination towards the elderly who are considered burdensome by young people. Interestingly, the hateful expressions towards the elderly were found more with regard to issues related to politics and economics and not just any content about the elderly. Conclusions. This study discussed the ways and means to enhance inter-generational understanding and solidity.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.113-128
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• 2021

In this study, we aimed to understand the public opinion on COVID-19 vaccine. To achieve the goal, we analyzed COVID-19 vaccine-related Twitter posts. 45,413 tweets posted from March 16, 2020 to March 15, 2021 including COVID-19 vaccine names as keywords were collected. The 12 vaccine names used for data collection included 'Pfizer', 'AstraZeneca', 'Modena', 'Jansen', 'NovaVax', 'Sinopharm', 'SinoVac', 'Sputnik V', 'Bharat', 'KhanSino', 'Chumakov', and 'VECTOR' in the order of the number of collected posts. The collected posts were analyzed manually and automatedly through keyword analysis, sentiment analysis, and topic modeling to understand the opinions for the investigated vaccines. According to the results, there were generally more negative posts about vaccines than positive posts. Anxiety about the aftereffects of vaccination and distrust in the efficacy of vaccines were identified as major negative factors for vaccines. On the contrary, the anticipation for the suppression of the spread of coronavirus following vaccination was identified as a positive social factor for vaccines. Different from previous studies that investigated opinions about COVID-19 vaccines through mass media data such as news articles, this study explores opinions of social media users using keyword analysis, sentiment analysis, and topic modeling. In addition, the results of this study can be used by governmental institutions for making policies to promote vaccination reflecting the social atmosphere.

• Informatization Policy
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• v.20 no.2
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• pp.73-90
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• 2013

Rational debate and public conversation in the public sphere of social network are crucial conditions for realizing deliberative democracy. However, negative communication can occur online more frequently than in the real space, and mutually hostile messages are appearing. In the electoral process, citizens combining for particular candidates have made personal attacks against, abused and slandered the opposing candidates. Then, how and to what degree has the flaming behavior been appearing in the elections? Are there influencers to propagate the flaming behavior? And how flaming are these influencers, compared to internet users? This research focuses on the flaming behavior which occurred during the reelection for Seoul Mayor, in order to diagnose the role of social network as an online public sphere. This study analyzes the spreading degree of flaming messages depending on each issue, and the differences of messages between influencers and normal users. There was frequent flaming behaviors to distribute biased information which criticized, laughed at and maliciously attacked individual candidates. Moreover, influencers who advanced leading opinions, displayed a higher flaming degree than normal users.