• 한국언론정보학보
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• 제27권
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• pp.7-34
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• 2004

본 연구는 국제뉴스에 대한 포괄적 관점을 바탕으로 한 기존의 논의들을 지양하고 보다 구체적이고 분석적인 시각에서 국제뉴스의 흐름을 살펴보고자 하였다. 국가 내 국제뉴스의 재분배 메커니즘을 살펴보기 위한 틀로서 본 연구는 정보 상품으로서 뉴스를 이해하고 분석하는 미디어 경제학적 논의를 근거로, 신문 시장 규모에 의한 국제뉴스 보도의 유의미한 차별화를 개념화하였다. 또한 기존의 제1세계와 제3세계 간 정보 불평등 논의가 시장 규모별로 어떻게 특성화되고 있는지를 밝혀보고자 하였다. 연구결과에 의하면 선정적 뉴스에 대해 신문사 유형(지역지, 전국지)은 유의미한 관계가 있는 것으로 밝혀졌다. 또한 공공적 뉴스보다 선정적인 뉴스가 제3세계 국가들에 대하여 더 많이 보도됨으로써, 국가간 정보 불평등 현상이 여전히 미해결 과제임을 본 연구는 지적하고 있다. 그리고 시장규모에 의한 신문사 유형이 제3세계 국가에 대한 보도량에 미치는 영향을 밝히고 있다. 마지막으로 본 연구는 방법론적 측면에서 시장규모에 의한 뉴스결정과 국제 정보질서 운동에 대한 논의는 기사의 수와 보도량 측면에서 차별화되어 논의되어야 함을 지적하고 있다.

• 분석과 대안
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• 제1권2호
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• pp.13-30
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• 2017

Referred to as a 'media war,' there is a fierce competition for media discourse between different countries. Twenty four hour global news channels like Al Jazeera, France 24, RT, NHK World, China's CCTV and teleSUR emerged to offer their own perspectives and stance in the global society, and to face the monopolization and distorted information created by the hegemony of English news channels which have swayed international public opinions for a long time. As a tool of public diplomacy, the media's role in determining the image of the nation and winning the 'Hearts and Minds' of the international community is decisive, but it cannot be said that they all have a similar influence or play a positive role in media diplomacy. A global news channel, which is both a media diplomatic subject and a journalism organization, can be in the position of acting as a public relations organization or a propaganda agency for the government depending on the regime's attitude because most of global news channels receive support from the government. Sometimes it is difficult for these media to implement quality journalism because of financial difficulties. Media discourse also has limitations in that it is dependent upon changes in foreign policy of its own government. This study examines the current status of global news channels, the dilemma these channels are facing, and suggests some potential directions that can be taken by global news channels in order to become more effective. It is becoming increasingly important for all nations to respond to distorted information about their own countries, to appropriately identify various issues and changes in the international community and to convey their views and positions to the international community. For now, there is a lack of awareness about the importance of media diplomacy in Korea: There are many English-language media, but as yet no global news channel which could have an influence on the international stage. However, there seems to be some understanding about the need for the media to present the Korean alternative discourse to the senseless dependency on Western media. We hope that this study will be an opportunity to think in depth about the attitude of the Korean global media, whether existing global media or new global news channels, in order to help them become more effective in media diplomacy.

• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.345-351
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• 2021

This study is designed to determine how to identify misleading news articles based on natural language processing on Artificial Intelligence & Bigdata. A misleading news discrimination system and method on natural language processing is initiated according to an embodiment of this study. The natural language processing-based misleading news identification system, which monitors the misleading vocabulary database, Internet news articles, collects misleading news articles, extracts them from the titles of the collected misleading news articles, and stores them in the misleading vocabulary database. Therefore, the use of the misleading news article identification system and methods in this study does not take much time to judge because only relatively short news titles are morphed analyzed, and the use of a misleading vocabulary database provides an effect on identifying misleading articles that attract readers with exaggerated or suggestive phrases. For the aim of our study, we propose news article identification methods in natural language processing on Artificial Intelligence & Bigdata.

• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.352-359
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• 2021

The purpose of this study is to design and build fake news discrimination systems and methods using fact-checking guidelines. In other words, the main content of this study is the system for identifying fake news using Artificial Intelligence -based Fact-checking guidelines. Specifically planned guidelines are needed to determine fake news that is prevalent these days, and the purpose of these guidelines is fact-checking. Identifying fake news immediately after seeing a huge amount of news is inefficient in handling and ineffective in handling. For this reason, we would like to design a fake news identification system using the fact-checking guidelines to create guidelines based on pattern analysis against fake news and real news data. The model will monitor the fact-checking guideline model modeled to determine the Fact-checking target within the news article and news articles shared on social networking service sites. Through this, the model is reflected in the fact-checking guideline model by analyzing news monitoring devices that select suspicious news articles based on their user responses. The core of this research model is a fake news identification device that determines the authenticity of this suspected news article. So, we propose news article identification methods with fact-checking guideline on Artificial Intelligence & Bigdata. This study will help news subscribers determine news that is unclear in its authenticity.

• International Journal of Advanced Culture Technology
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• 제4권3호
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• pp.62-68
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• 2016

This study explores evaluative linguistic expressions in news reporting about the 2016 general election outcome in Korean newspapers. In particular, we have examined the evaluative linguistic expressions quoted from the three Western news media -New York Times, Washington Post, and BBC, both quantitatively and qualitatively in Korean news stories in order to know how journalists frame the news stories to persuade news consumers to accept their ideologies. This is based on the assumption that quotation can be a tool in conveying ideologies to news consumers (van Dijk, 1988, Jullian, 2011). To achieve this purpose, we selected ten Korean newspapers which included quotations from the news stories of the three Western media and then analyzed the quoted expressions quantitatively and qualitatively. For a qualitative analysis, evaluative linguistic expressions were analyzed to examine the journalistic stances of the Western news stories, following Martin's (2003) appraisal theory. For a quantitative analysis, a word frequency analysis was conducted to figure out the ratio of quoted words to the whole news texts in Korean newspapers. As a result, it was found that the news stories of BBC and Washington Post were more frequently quoted than that of New York Times when journalists conveyed neutral or positive attitude to the election outcome, thus confirming that evaluative linguistic expressions were functionally employed to convey journalists' ideologies or stances to news readers.

• International Journal of Advanced Culture Technology
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• 제6권2호
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• pp.65-71
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• 2018

In the digital era, news consumption is not confined in geological boundaries. Technological advances bring the instant dissemination of news into life and allow news audience to consume events that occur far away almost in real time. The transmission has blurred the boundary between traditional media and new media, and the one between physical and virtual world. That is, what if a journalist applies news framing to the news translation process? This paper aims to investigate the gap between the ST and the TT created when the source news texts undergo a translation process. To achieve this aim, the appraisal theory developed by White (2003) is employed to identify a difference between the ST and the TT. Furthermore, we have attempted to identify differences between soft news stories and hard news stories while the STs from both news stories are translated into the TTs. Two time-sensitive events, Hugh Grant's marriage and a U.S. and North Korea summit, were selected. The former (a soft news story) is extracted from the Telegraph and the latter (a hard news story) is from the Washington post. As a result, it was found that such strategies as attitude, engagement, and judgment were used when the source news texts from the hard news story are translated into the target news texts. Under the appraisal theory, the strategies involve evaluative language which refers to positive or negative language that judges the worth of entities. In general, it is said that a journalist frames the SS (especially from the hard news story) to convey his ideology to news consumers. Hypothetically, we assume that a similar framing process takes place in deriving the TT from the SS of the hard news story. Thus, we could conclude that the TT from the hard news story differs from the TT from the soft news story and that the difference can be explained within the framework of White's appraisal theory.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.1-8
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• 2022

This work provides a reliable and classified stocks dataset merged with Saudi stock news. This dataset allows researchers to analyze and better understand the realities, impacts, and relationships between stock news and stock fluctuations. The data were collected from the Saudi stock market via the Corporate News (CN) and Historical Data Stocks (HDS) datasets. As their names suggest, CN contains news, and HDS provides information concerning how stock values change over time. Both datasets cover the period from 2011 to 2019, have 30,098 rows, and have 16 variables-four of which they share and 12 of which differ. Therefore, the combined dataset presented here includes 30,098 published news pieces and information about stock fluctuations across nine years. Stock news polarity has been interpreted in various ways by native Arabic speakers associated with the stock domain. Therefore, this polarity was categorized manually based on Arabic semantics. As the Saudi stock market massively contributes to the international economy, this dataset is essential for stock investors and analyzers. The dataset has been prepared for educational and scientific purposes, motivated by the scarcity of data describing the impact of Saudi stock news on stock activities. It will, therefore, be useful across many sectors, including stock market analytics, data mining, statistics, machine learning, and deep learning. The data evaluation is applied by testing the data distribution of the categories and the sentiment prediction-the data distribution over classes and sentiment prediction accuracy. The results show that the data distribution of the polarity over sectors is considered a balanced distribution. The NB model is developed to evaluate the data quality based on sentiment classification, proving the data reliability by achieving 68% accuracy. So, the data evaluation results ensure dataset reliability, readiness, and high quality for any usage.

• International Journal of Contents
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• 제12권3호
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• pp.1-11
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• 2016

This exploratory study examines the critical factors associated with news consumers' perception of information overload and news consumption patterns. An online survey was conducted with Qualtrics panels (N = 1001). The demographics and three antecedent factors of perceived information overload were considered including the frequency of news access through multiple media platforms, level of attention to news, and interest in news. Three news consumption patterns were investigated as possible consequences of perceived information overload: news avoidance, selective exposure, and willingness to pay for news. The results of hierarchical regression analyses revealed a meaningful distinction between general and news information overload. Overall, news consumers who paid more attention to news through newer media/platforms/devices perceived higher levels of information overload, were more willing to pay for the news, and often avoided news or selectively exposed themselves to certain sources of news to manage news information overload.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.847-854
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• 2022

The aim of this study is to develop a Named Entity Recognition (NER) model to automatically identify construction-related organizations from news articles. This study collected news articles using web crawling technique and construction-related organizations were labeled within a total of 1,000 news articles. The Bidirectional Encoder Representations from Transformers (BERT) model was used to recognize clients, constructors, consultants, engineers, and others. As a pilot experiment of this study, the best average F1 score of NER was 0.692. The result of this study is expected to contribute to the establishment of international business strategies by collecting timely information and analyzing it automatically.