• Journal of Hospice and Palliative Care
• /
• v.13 no.4
• /
• pp.209-215
• /
• 2010

• 축산식품과학과 산업
• /
• v.1 no.2
• /
• pp.2-13
• /
• 2012

• 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.

• Human Ecology Research
• /
• v.59 no.1
• /
• pp.23-43
• /
• 2021

This study looked through the text mining analysis to check the status of the virtual assistant service, and explore the needs of consumers, and present consumer-oriented directions. Trendup 4.0 was used to analyze the keywords of AI services in Online News and social media from 2016 to 2020. The R program was used to collect consumer comment data and implement Topic Modeling analysis. According to the analysis, the number of mentions of AI services in mass media and social media has steadily increased. The Sentimental Analysis showed consumers were feeling positive about AI services in terms of useful and convenient functional and emotional aspects such as pleasure and interest. However, consumers were also experiencing complexity and difficulty with AI services and had concerns and fears about the use of AI services in the early stages of their introduction. The results of the consumer review analysis showed that there were topics(Technical Requirements) related to technology and the access process for the AI services to be provided, and topics (Consumer Request) expressed negative feelings about AI services, and topics(Consumer Life Support Area) about specific functions in the use of AI services. Text mining analysis enable this study to confirm consumer expectations or concerns about AI service, and to examine areas of service support that consumers experienced. The review data on each platform also revealed that the potential needs of consumers could be met by expanding the scope of support services and applying platform-specific strengths to provide differentiated services.

• International journal of advanced smart convergence
• /
• v.12 no.1
• /
• pp.164-172
• /
• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• Korea Trade Review
• /
• v.47 no.2
• /
• pp.121-132
• /
• 2022

This study aims to find out the ESG management keywords in the logistics industry through social network analysis using news article and sustainable management reports. In recent years, global climate change and Covid-19 have spurred companies to step up their new management system called ESG management. ESG is a combination of Environment, Social, and Governance. In the past, companies' financial performance was the most important, but in the current investment market, the movement to reflect ESG management factors in investment decisions is strengthening. This study aims to find out degree centrality, betweenness centrality, and closeness centrality through social network analysis after collecting related keywords to derive ESG management issues of logistics companies. This study collected 2,359 news articles searched under the keywords "ESG", "Logistics". In addition, data on ESG activities were also used for analysis by referring to the sustainable management reports of logistics companies. As a result of the analysis of degree centrality, it was found that ESG management of logistics companies is in progress, focusing on small enterprises and eco-friendly keywords, and is concentrated on social responsibility and eco-friendly activities. In the betweenness centrality analysis, logistics companies such as HMM and CJ Logistics were derived in a high ranking. In the closeness centrality analysis, eco-friendly keywords topped the list, while the number of keywords related to governance was relatively small, suggesting that logistics companies need to improve their governance structure.

• Journal of Internet Computing and Services
• /
• v.17 no.4
• /
• pp.135-143
• /
• 2016

The Internet is being utilized as a means of communication channel for science information. In this study, an experiment was conducted against college students, in which participants were assigned to one of the following four conditions: source status(professor, student) x social categorization(in-group, out-group). When the participants finished reading an online news article, perceived usefulness of and attitude toward the science technology were measured. The results show that the effect of source expertise on perceived usefulness was moderated by social categorization.

• Journal of Contemporary Eastern Asia
• /
• v.20 no.2
• /
• pp.108-128
• /
• 2021

Research on the news coverage of North Korea has been paying less attention to social media platforms than to legacy media. An increasing number of social media users post, retweet, share, interpret, and set agendas on North Korea. The accessibility of international users and North Korea's publicity purposes make social media a venue for expression, news diversity, and framing about the nation. This study examined the sentiment of Twitter posts on North Korea from a framing perspective and the relationship between network strengths and sentiment from a social network perspective. Data were collected using two tools: Jupyter Notebook with Python 3.6 for preliminary analysis and NodeXL for main analysis. A total of 11,957 tweets, 10,000 of which were collected using Python and 1,957 tweets using NodeXL, about North Korea between June 20-21, 2020 were collected. Results demonstrated that there was more negative sentiment than positive sentiment about North Korea in the sampled Twitter posts. Some users belonging to small network sizes reached out to others on Twitter to build networks and spread positive information about North Korea. Influential users tended to be impartial to sentiment about North Korea, while some Twitter users with a small network exhibited high percentages of positive words about North Korea. Overall, marginalized populations with network bonding were more likely to express positive sentiment about North Korea than were influencers at the center of networks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.1
• /
• pp.51-73
• /
• 2023

Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNetB0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

• Korean journal of communication and information
• /
• v.39
• /
• pp.385-427
• /
• 2007

This study examined the primary tones of news stories and the overall frames which are structuralized by the primary tones in the news reporting of the Private School Law and social conflicts occurring around the law. For this purpose, the study applied Lakoff and Johnson's metaphoric conceptualization to the analysis of the news stories reported in the Chosun Ilbo, the Hankyoreh, and the Kookmin Ilbo, which are considered to represent the audience of the conservative, progressive, and religious forces, respectively. The main goal of this study includes to describe in which manner the newspapers attempt to depict the frames of major social conflicts regarding the Private School Law. The results show that (1) the Chosun Ilbo and the Kookmin Ilbo attempt to structuralize the social conflicts by providing frames of 'freedom is an asset' and a 'war' metaphors; (2) the Kookmin Ilbo applied more frames of a religious metaphor among others; and (3) the Hankyoreh attempts to structuralize the social conflicts by offering frames of 'the front is good, but the rear is bad' and 'war' metaphors, which proves to be the opposite in presenting the overall framing.