• Korean journal of communication and information
• /
• v.62
• /
• pp.223-243
• /
• 2013

Value collision takes place in the news organization at times. The concept of social responsibility has been serving as the raison d'$\hat{e}$tre not just of individual reporters but eventually of journalism in general. But when the organizational interests are at stake, more realistic values contributing to the organization often override the normative values. This phenomenon occurs since there exists a clear discrepancy between the nominally highlighted values and the virtual values of press owner or news organization. Focusing on Chosun Ilbo, this paper tries to identify the values a news organization really underlines and pursues, and explores the nature of organizational culture embedded in these values. In specific, this study analyzes such performances as ceremonies and rituals, regular or occasional, in Chosun Ilbo, since these are cultural practices which produce and circulate particular meanings and values forming the basis of organizational culture. According to this study, 'strong unity' is the core value which the owner family in Chosun Ilbo has ever been spotlighting. As long as the strong unity is stressed, the role playing as members of news organization for its interest is more highly regarded than carrying out the normative missions given unto the journalists in general.

• The Journal of Information Systems
• /
• v.32 no.2
• /
• pp.87-108
• /
• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.294-302
• /
• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of Family Relations
• /
• v.22 no.4
• /
• pp.107-134
• /
• 2018

Objectives: The purpose of this study is to have diachronic understanding of holiday stress that has become the social issues through the analysis on the news articles about holiday stress from 1993 to 2016. Method: For this purpose, 416 articles and 457 cases about holiday stress from 5 daily newspapers such as Chosun Ilbo, Joongang Ilbo, Dong-A, Hankyoreh and Kyunghyang Shinmun etc. have been analyzed, conducting the qualitative and quantitative analysis together. Results: Firstly, the articles on holiday stress have been increased, showing the rapid increase per year for the last 20 years. It is presumed to be closely related to the socio-economic situation. Second, although there have been 'married women' overwhelmingly as the subject of holiday stress, the frequency of the young generation has been increasing recently including the 'married women'. Third, the 96.7% of the contents from psychological appeal appeared in the case of holiday stress is related to family values. Especially, the holiday stress related to 'value of patriarchy' was the biggest stress. However, there has been increasing holiday stress caused by 'value of kinship' and 'value of marriage' recently. Forth, as a countermeasure against the holiday stress, the 'perception on the change of family values' has been quantitatively suggested and it has become actively appeared in terms of contents after mid-2000s. However, it has been appeared low in terms of quantity and content recently. Conclusions: This study has significance since it has been verified that the holiday stress started from 'married women' but it has been expanded to the young generation and it is related to the change and co-existence of family values of our society.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.1-8
• /
• 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.

• Korean journal of communication and information
• /
• v.71
• /
• pp.9-32
• /
• 2015

The media, traditionally, serves to reinforce one's limited memory and transform those personal memories of society's members into collective memories. Notably, the mass media collects countless pieces of personalized memories for the creation of collective memories. Through the process of recollecting as well as recreating the past in the present, mass media exerts influence on the means the public appreciates and understands the history. Although numerous new medias like Internet overflows in today's society, television continues to stand firm as the salient means to construct the memories in daily lives. In this context, the research aims to analyze the televised news as the principal agent of memory producer to determine through which memories it recreates the $5{\cdot}18$ in today's media. The analysis of news values clarifies that every government placed distinctive news values on $5{\cdot}18$ within its historical context. Even so, such values were often fixed based on its relations to the existing political issues. Furthermore, through the discourse analysis, this research concludes that today's coverage of $5{\cdot}18$ is softening and becoming conventional.

• Journal of Korean Society of Rural Planning
• /
• v.25 no.4
• /
• pp.13-26
• /
• 2019

The rural areas in South Korea have changed rapidly in the process of national land development. Rural landscapes have become discoloured, and their attractiveness has decreased as cities have expanded. But the attractiveness or multifunctional values of rural areas has become more important in contemporary society around the world. According to this social demand, the efforts of conserving the rural landscape are of high priority and the recovery of ruralism in the area is required. This study has tried to understand how the public image of ruralism in South Korea has been influenced by the news media. The study retrieved news articles using the web searching portal site from the six keywords, commonly used to refer to ruralism, including 'rural landscape', 'rural community', 'rural tourism', 'rural life', 'rural amenity', and 'rural environment'. News data from the six keywords were also collected respectively from within the year-period of 2004-05, 2007-08, 2012-13, and 2016-17. In the text mining analysis, the nouns with high Degree Centrality were figured out, and the changes by year-period were identified. Then, LDA topic analysis was performed for text datasets of six keywords. As a result, the study found that the news articles gave an informed focus on only a handful of issues such as 'poor rural living condition', 'regional or village improvement projects', 'rural tourism promotion projects', and 'other government support projects'. On the other hand, nouns related to virtues and values in the rural landscape were less shown in news articles. These results have become more apparent in recent years. In the topic analysis, 35 topics were identified. 'village development projects', 'rural tourism', and 'urban-rural exchange projects' were appeared repeatedly in several keywords. Among the topics, there are also topics closely related to ruralism such as 'rural landscape conservation', 'eco-friendly rural areas', 'local amenity resources', 'public interest values of agriculture', and 'rural life and communities'. The study presented an image map showing ruralism in South Korea using a network map between all topics and keywords. At the end of the study, implications for Korean rural area policy and research directions were discussed.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.3
• /
• pp.154-161
• /
• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Bulletin of the Korean Space Science Society
• /
• 2008.10a
• /
• pp.22.1-22.1
• /
• 2008

Media company is not a business which sells news and information but a company sells audience's attention. Advertisers estimate audience's quantity and quality which pay attention to media and pay the cost. Thus drawing audience's attention is a natural and instinctive behavior for survival of media company. News doesn't deliver the fact just as it. That's impossible. News is a commodity made by processing and selection of the media company and journalist. On the process, judge of values is involved and limitation of time and place of media is considered. If scientists understand media's character truly, their misunderstanding about media company and journalist may be cleared up. In this society, media is not a being to ignore, particularly for big science like space science which spends huge public capital. Nowadays, space science meets the time to take the leap in Korea. However that can be crisis cause of uncertainty of science activity. When the crisis which no one desires happens, preparation needs for new opportunity. We can take the crisis as a chance. Understanding about media and public will be the first step for this preparation.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.3
• /
• pp.697-704
• /
• 2007

It is common in financial time series that volatility(conditional variance) as a measure of risk exhibits asymmetry in such a manner that positive and negative values of return rates of the series tend to provide different contributions to the volatility. We are concerned with asymmetric conditional variances for Korean financial time series especially during the time span of 2000-2001. Notice that these periods suffer from 9-11 disaster in US and collapses of stock prices of dot-companies in Korea. Threshold-ARCH models are considered and a Wald test of asymmetry is suggested. News impact curves are illustrated for graphical representations of leverage effects inherent in various Korean financial time series.