• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.89-91
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• 2017

A currency exchange is the rate to exchange currencies between different countries and the one of important factors to measure the economic size or status of a country. The currency exchange is affected by various economic or social events and changed dynamically. However, since too many economic and social factors affect the exchange rate and the leverage rate of each factor is so floating, it is difficult to define clearly the relationships between the exchange rate and the specific factor. In this paper, we analyze the data pattern for the exchange rate and news articles. To do this, we counts the frequencies of words presented in the news articles during specific periods and compare the frequencies with the margins of exchange rates.

• Journal of Hydrogen and New Energy
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• v.33 no.2
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• pp.121-129
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• 2022

With the advent of the post-2020 climate regime, the hydrogen industry is growing rapidly around the world. In order to build the hydrogen economy, it is important to identify social issues related to hydrogen and prepare countermeasures for them. Accordingly, this study conducted a semantic network analysis on hydrogen news from NAVER. As a result of the analysis, the number of hydrogen news in 2020 increased by 4.5 times compared to 2016, and as of 2018, the hydrogen issue has shifted from an environmental aspect to an economic aspect. In addition, although the initial government-led hydrogen industry is expanding to the mobility field such as privately-led fuel cell electric vehicles and hydrogen fuel, terms showing concerns about the safety such as explosions are constantly being exposed. Thus, it is necessary not only to expand the hydrogen ecosystem through the participation of private companies, but also to promote hydrogen safety.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.41-75
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• 2018

The purpose of this study is to analyze the characteristics of Korean media on the topic of archives & records management based on time-series analysis. In this study, from January, 1999 to June, 2018, 4,680 news articles on archives & records management topics were extracted from BigKinds. In order to examine the characteristics of the media coverage on the archives & records management topic, this study was analyzed to the difference of the press coverage by period, subject, and type of the media. In addition, this study was conducted word-frequency based content analysis and semantic network analysis to investigate the content characteristics of media on the subject. Based on these results, this study was analyzed to the differences of media coverage by period, subject, and type of media. As a result, the news in the field of records management showed that there was a difference in the amount of news coverage and news contents by period, subject, and type of media. The amount of news coverage began to increase after the Presidential Records Management Act was enacted in 2007, and the largest amount of news was reported in 2013. Daily newspapers and financial newspapers reported the largest amount of news. As a result of analyzing news reports, during the first 10 years after 1999, news topics were formed around the issues arising from the application and diffusion process of the concept of archives & records management. However, since the enactment of the Presidential Records Management Act, archives & records management has become a major factor in political and social issues, and a large amount of political and social news has been reported.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.

• Korean journal of communication and information
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• v.71
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• pp.194-223
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• 2015

This study explores a value chain structure of TV program including news, drama, and comedy. The purpose of this research focused on factor analysis and the relationship among viewer's program selection motivations. This research explores correlation between personal value and viewing motivation. This study was to identify the value structure of respondent on TV program(news, drama, comedy) based on means-end chain theory. The research used structured APT laddering questions and 251 data was analysed. Through such analysis, category difference by stage and relationship difference were identified and hierarchical value map was compared. There are four different value ladders: first is attributes, functional consequences, psychological consequences, and final value. The result shows that on news program the basic function is viewers are want to visual factor and quickly acquire social news and they pursue a value of personal social relationship. Whereas, on drama program, the viewers are reflected by around person, and they selected a program based on closed related person. In addition, the viewers are influenced by program's social nomination, production's brand in drama, and performer's nomination, producer and program prominence on comedy. The program selection is highly correlated on program selection's credibility, vital energetic life, and social relationship. The results shows that there was no significant difference between low involvement group and high involvement group for main category involvement group respondents.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.448-459
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• 2021

In this paper, we propose a method of predicting the next-day stock price fluctuations of 10 KOSDAQ-listed companies in 5G, autonomous driving, and electricity sectors by training SVM, XGBoost, and LightGBM models from macroeconomic·financial market indicators, technical indicators, social interest indicators, and daily positive indices extracted from online news. In the three experiments to find out the usefulness of social interest indicators and daily positive indices, the average accuracy improved when each indicator and index was added to the models. In addition, when feature selection was performed to analyze the superiority of the extracted features, the average importance ranking of the social interest indicator and daily positive index was 5.45 and 1.08, respectively, it showed higher importance than the macroeconomic financial market indicators and technical indicators. With the results of these experiments, we confirmed the effectiveness of the social interest indicators as alternative data and the daily positive index for predicting stock price fluctuation.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• Journal of Information Science Theory and Practice
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• v.11 no.1
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• pp.1-13
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• 2023

This study determines how social media users (SMUs) verify the information they come across on the Internet. It determines SMUs' perception of online fact-checking services in terms of their ease of use, usefulness, and trust. By conducting a focus group discussion and key informant interviews, themes were derived in determining fact-checking practices while a survey was further conducted to determine such perceived ease of use, usefulness, and trust in fact-checking services. The thematic analysis revealed major information verification practices, such as cross-checking and verifying with other sources, inspecting comments and reactions, and confirming from personal and social networks. The results showed that SMUs considered fact-checking services easy to use. However, a concern was raised about their usefulness stemming from the delayed action in addressing the information issues that need to be verified. As to perceived trust, it was found that SMUs have reservations about fact-checking services. Finally, it is believed that fact-checking services are expected to be credible and need to be promoted to mitigate any form of fake news, particularly on social media platforms.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.377-394
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• 2014

This study conducted a frame analysis of the news reports of Chonbuk bus strike broadcasted via local broadcasting companies of the Jeollabuk-do. The results showed that all Chonbuk local televisions gave more emphasis on 'episodic' frame by emphasizing demonstrations and struggles between capital and labor. In the case of content frames, news reports did not present the solution or an alternative of the strike, and the democratic process frame or the institutional improvement frame was not embossed. Therefore, local television news of Chonbuk bus strike was insufficient in reporting causes and backgrounds of the strike. Also, it displayed existing press routine in reporting the issue of social conflict such as a bus strike.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.1-12
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• 2020

The purpose of this study is to explore the trend of blockchain technology through analysis of patents and news articles using text mining, and to suggest the blockchain policy agenda by grasping social interests. For this purpose, 327 blockchain-related patent abstracts in Korea and 5,941 full-text online news articles were collected and preprocessed. 12 patent topics and 19 news topics were extracted with latent dirichlet allocation topic modeling. Analysis of patents showed that topics related to authentication and transaction accounted were largely predominant. Analysis of news articles showed that social interests are mainly concerned with cryptocurrency. Policy agendas were then derived for blockchain development. This study demonstrates the efficient and objective use of an automated technique for the analysis of large text documents. Additionally, specific policy agendas are proposed in this study which can inform future policy-making processes.