• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.7-14
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• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.49-56
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• 2018

Internet news consumption is rapidly growing in Korea, and majority of that is being done through Naver, Korea's primary search engine. Naver is also the go-to search engine for smartphone use. This study analyzed 824 most popular news accessed via mobile gears; the news items were selected from Naver's 'Daily Top 10 Stories,' dating from March 2016 to December 2016. The results indicate that entertainment news were the most viewed, while political and social issue news were the most liked and commented by mobile users. With regard to news value, 'prominence' and 'impact' were the two most important factors that influenced a user's news selection process in a mobile environment. The degree of a news' 'prominence' was the most important factor that determined the number of views, while 'impact' was critical to determining "the most commented-upon" and "the most liked" news. The results also indicate that mobile news consumers prefer more dramatic storylines and events that incite public anger or grief, threaten the safety of citizens, or evoke emotional sympathy rather than 'hard news' about such subjects as politics and economics.

• Journal of Information Management
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• v.41 no.2
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• pp.161-181
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• 2010

The purpose of this study is to investigate citation behavior in internet news to research papers of 10 domestic economic institutes and to suggest institutes' impact quantitatively with h-index and various modified indices. Content analysis of 878 news articles that collected from NAVER news site was performed. First, as citing behavior, cited numbers of research papers, preferred news media, speed, source entry accuracy, centrality, subject section, and length by the institutes were examined. Next, impact indices for institutes were calculated by cited numbers using h-index, g-index, $h_s$-index, and $g_s$-index, and the ranking of 10 research institutes were determined by each impact indices. As a result, institutes belonged to upper ranks showed little variation among the different indices. On the other hand, institutes belonged to middle and lower ranks showed variations in impact indices and experts' survey.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.383-391
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• 2017

The purpose of this study was to examine users' attitude toward likability, understanding level, reliability, satisfaction, and continuous usage intention depending on a difference in a type between card news and straight news. A questionnaire survey was conducted targeting 232 people. As a result of the research, compared to the straight news, the card news was easy for a user to understand, was strong even in a desire to use continuously. Second, a factor of users' attitude toward news was having influence upon the mutually positive(+) direction. Likability, understanding level and reliability had an effect on satisfaction. The satisfaction had an impact again on continuous usage intention. The intensity of this impact was varied, respectively, in card news and straight news. The influential level upon satisfaction in card news was strong in order of likability, understanding level and reliability. The influence in the straight news was strong in order of reliability, likability and understanding level. The outcome of this study will become empirical data in proceeding with seeking a method available for strengthening the function of offering information in news through increasing delivery and impact in information with producing news chosen by consumers.

• International Commerce and Information Review
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• v.10 no.1
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• pp.395-414
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• 2008

The objective of this study is to introduce the concepts and theories of conditional heteroscedastic volatility models and the news impact curves and apply them to the Korea inbound tourism market. Three volatility models were introduced and used to estimate the conditional volatility of monthly arrivals of inbound tourists into Korea and news impact curves according to the three models. Results of this study are as follows. As the proportion of American tourists occupied a large amount of Korea inbound tourism market, the markets' forecasting is very important. The news impact curves which used EGARCH model (1,1) and TGARCH model(1,1), with data on these tourists to Korea showed an asymmetry effect of volatility. It was common that bad news means that it was estimated more sensitively than good news. From these results, we will notice that American tourists who visited Korea only for tourism are affected by good news. The result suggests that the Korea government and tourism industry should pay more attention to changes in the tourism environment following bad news because conditional volatility increases more when a negative shock occurs than when a positive shock occurs.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• Atmosphere
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• v.30 no.3
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• pp.237-248
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• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.157-171
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• 2014

As electronic word of mouth plays an important role in purchase behavior among consumers, the number of studies on the impact of electronic word of mouth is rapidly increasing. Nevertheless, it is difficult to discover comparative studies on the mass media which had a great impact on consumer's purchase behavior before the impact of electronic word of mouth becomes greater versus the social media where electronic words of mouth are created and distributed. It is considered that it seems to be necessary to find an appropriate mutual supplement point between the media designed for a successful marketing by comparing and analyzing the existing mass media versus the social media, major media for electronic word of mouth. Therefore, this study aims to compare and analyze the impact of comments on movie revenue in the representative forms of mass media such as online news and social media blogs. In particular, this study also considers an appropriate media for promoting movies by period by comparing and analyzing the two media before and after film release. For analysis, this study collects the information on the number of comments on online news and blogs in 70 Korean movies released in 2011 and 2012 from five weeks before film release to eight weeks after film release on a daily basis via Naver. This study also collects the information on the movie revenue using the statistical data of movie industry from Korean Film Commission. As a result of empirical data analysis, it is found that the two media showed no difference in movie revenue before film release, but after film release, the impact of blogs was more significant than that of online news.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.175-194
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• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.

• Journal of Internet Computing and Services
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• v.19 no.4
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• pp.123-133
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• 2018

This paper examined how the millennial generation consumes mobile news through social networking sites with regards to user patterns, preference topics and news values, and whether news topics and news values may influence their overall mobile SNS news consumption and interactivity. The findings show that more than 2/3 of respondents consumed mobile SNS news at least once everyday for 30minutes to one-hour. Male millennials tended to use Facebook and Kakao-talk more than female. While the portal site was the most accessed channel for consuming mobile news, SNS was the second, more than the combined use of national daily papers, TV, and internet newspapers. The respondents' demographic characteristics and news topics also affect the form and degree of news interactivity. With regards to their preferences and prioritization of news values, millennials tend to perceive 'impact' and 'usefulness' as being most important, despite the differences of their demographic characteristics. They also preferred those news values most. There were significant differences in terms of preferred news topics according to the demographics' characteristics.