• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4958-4970
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• 2019

As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.

• Korean journal of communication and information
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• v.27
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• pp.7-34
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• 2004

This study tried to examine the internal mechanism in selecting, sorting, and producing international news coming from foreign country. With a perspective of media economists, this study hypothesized a relationship between the type of newspaper(regional vs. national), based on news markets, and the type of international news(sensational vs. public affairs), between the type of country(the First vs. Third world) and the type of news, and between the type of news market and the type of nation). The results indicated a signifiant relationship between the type of press and the type of news, with some internal inconsistency on the number of article and the amount of coverage. Further, this study revealed there was still an imbalance between the First and the Third country in terms of rho quality and quantity of international news. Also a significant relationship was found between the type of newspaper and the type of country.

• Asia pacific journal of information systems
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• v.31 no.4
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• pp.606-632
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• 2021

With many consumers being exposed to news via social media platforms, news organizations are challenged to attract visitors and generate revenue during visits to their websites. They therefore need detailed information on how to write articles and headlines to increase visitors' engagement with the content to drive advertising revenues. For those news organizations whose business model depends mainly on advertisements, rather than subscriptions, it is particularly crucial to understand what makes the website attractive to their visitors, what drives users to stay on the website, and what factors affect a user's exit decision. The current research examines individual news consumers' choices to find patterns of increase or decrease in user engagement relative to a variety of topics, as well as to the mood or tone of the content. Using clickstream data from a major news organization, the authors develop a user-level dynamic model of clickstream behavior that takes into account the content of both headlines and stories that visitors read. The authors find that readers appear to exhibit state dependence in the tone of the articles that they read. They also show how the topics expressed in headlines can affect the amount of content readers consume when visiting the news organization to a much larger degree than the topics expressed in the content of the article. Online publishers can make use of such findings to present visitors with content that is likely to maintain and/or increase their engagement and consequently drive advertising revenue.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.575-582
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• 2020

We proposes a method to estimate political preference of users who write comments on internet news. We collected and analyzed a massive amount of new comment data from internet news to extract features that effectively characterizes political preference of users. We expect that it helps user to obtain unbiased information from internet news and online discussion by providing estimated political stance of news comment writer. Through comprehensive tests we prove the effectiveness of two proposed methods, lexicon-based algorithm and similarity-based algorithm.

• Information Systems Review
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• v.23 no.4
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• pp.127-146
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• 2021

Fake news isexpanded and reproduced rapidly regardless of their authenticity by the characteristics of modern society, called the information age. Assuming that 1% of all news are fake news, the amount of economic costs is reported to about 30 trillion Korean won. This shows that the fake news isvery important social and economic issue. Therefore, this study aims to develop an automated detection model to quickly and accurately verify the authenticity of the news. To this end, this study crawled the news data whose authenticity is verified, and developed fake news prediction models using word embedding (Word2Vec, Fasttext) and deep learning algorithms (LSTM, BiLSTM). Experimental results show that the prediction model using BiLSTM with Word2Vec achieved the best accuracy of 84%.

• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.99-105
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• 2015

The growth of internet infrastructure and a tremendous increment of internet users lead actively to found internet newspaper publishing companies, which are able to dig up and publish own news articles. In disregard of these quantitative growth of internet newspaper companies, the qualitative growth of them doesn't coincide with the quantitative growth. Therefore, to require social responsibility and to build healthy media environment, Korean government has put in force registration system of internet newspaper company. According to this system, internet newspaper companies have to produce at the inside over 30 percent of weekly publications, and this requisite increases the needs of its verification. This paper investigates technologies to measure the self-supplied news volumes of internet newspaper company, examines validity of them, and presents appropriate method to measure. To compare huge amount of news articles rapidly, the presented method is based on the modified edit-distance, which reflects human cognition of word and empirical information related with it. To prove correctness of our presented method, we show experimental results for some real internet news articles.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.1-7
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• 2015

Although data on the internet can be used in various fields such as source of data of IR(Information Retrieval), Data mining and knowledge information servece, and contains a lot of unnecessary information. The removal of the unnecessary data is a problem to be solved prior to the study of the knowledge-based information service that is based on the data of the web page, in this paper, we solve the problem through the implementation of XTractor(XPath Extractor). Since XPath is used to navigate the attribute data and the data elements in the XML document, the XPath analysis to be carried out through the XTractor. XTractor Extracts main text by html parsing, XPath grouping and detecting the XPath contains the main data. The result, the recognition and precision rate are showed in 97.9%, 93.9%, except for a few cases in a large amount of experimental data and it was confirmed that it is possible to properly extract the main text of the news.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.2
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• pp.19-37
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• 2020

There are limited research questioning the relationship between the disabled community and media, particularly in Malaysia. The lack of awareness and common assumption of specialty towards the disabled community have caused a small amount of local disability researches that question the relationship between the disabled community and the media. This research aims to find out the types of representation of disability in the Malaysian mainstream media, particularly press. Interview with visual disabled personnel and content analysis from news coverage of mainstream press are deployed in the study. The findings indicated the invalidity of disability culture as the misrepresentation of disabled community in Malaysia is not as severe as depicted by scholars because the news coverage focusing on them is getting more positive. Besides that, disabled people are not defensive towards the terms used to refer them as long as media practitioners do not over amplify their disability. The application of charity approach is still common in news coverage to portray the disabled community as victim, and therefore they are partially marginalized due to the misrepresentation in Malaysian mainstream press.

• Korean journal of communication and information
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• v.74
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• pp.109-135
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• 2015

This study investigated the news report tendency and frame by three major Korean television networks(KBS, MBC, and SBS) in their news reports on issues involving their own interests and considering in social level. The three issues chosen for this study are 'advertising regulations for total amount', 'the reallocation of 700MHz spectrum' and 'raising TV license fee'(this issue applicable only to KBS). By using content analysis method, this study identified extremely weighted tendency toward themselves by the network television channels. They have used highly biased tone and sources to reinforce their private interests in news reports about the three controversial issues. In terms of story content, the news reports have used attribution of responsibility frame dominantly in 'advertising regulations for total amount' issue, whereas the moral frame was dominant in the other two issues.