• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Korean journal of communication and information
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• v.58
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• pp.7-30
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• 2012

The advent of Social Journalism coincided with the rise of social media to create and deliver news information; as a type of civic journalism, social journalism may be characterized as a new form of information gathering and news reporting which is fed by citizens creating news information through their use social networking services (SNSs). The current study analyzed a Facebook page called, to determine how this page was utilized during the onset of the citizen movement for the Egyptian democratic revolution to produce news, to facilitate interaction among the public and to deliver the news under the form of networked journalism. Each post uploaded onto the Facebook page from January 27 till February 2, 2011 was coded in its category, content and the contextual frame of the news. The results of the study showed that during the first week, straight news rather than those with opinions was produced most frequently. The research findings of the current study suggest that in a society of political turmoil, such as in Egypt and other Arabic countries, when the institutionalized media are controlled severely by the government or other forces, SNSs can perform journalistic media roles which create and distribute news information representing facts and reality, and simultaneously facilitate the public's interactions on social and political issues.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.459-468
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• 2022

This study searched and analyzed news and information on the Korea-U.S. Summit to explore which political agenda is spreading among Korean newspapers and social media. The result of the analysis revealed that, on the one hand, the conservative-leaning newspaper, Chosunilbo, covered the unresolved issue between two countries. The principal source of news was the opposition party. On the other hand, the progressive-leaning newspaper, Kyunghany Sinmun, highlighted President Moon's visit to the United States and described the visit to the United States as an achievement. In this paper, the principal source of news is the ruling party. Both conservative and the progressive newspapers did not present a negative view of the United States. In the case of Chosunilbo, it mentioned that foreign policy priority of President Biden is human rights in North Korea. If the two countries do not solve this issue, the relationship between Korea and the United States will not develop further. Second, I searched YouTube videos about the Korea-U.S. summit and conducted a network analysis to understand the influence of YouTube videos and explore their relationship the each other. The results of the analysis revealed that the 10 most influential videos portrayed the Moon government positively. These videos held the achievement of the visit to the United States in highly esteem and framed it positively, similarly to the progressive newspaper.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.285-302
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• 2013

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

• Korean journal of communication and information
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• v.84
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• pp.152-188
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• 2017

This study stereotyped the media on the basis of ideological inclinations and media types and explored the news coverage through word analysis, network analysis, and frame analysis. There was no difference between conservative media and progressive media in terms of the amount of news. The conservative mainstream media considered the THAAD rumor as an unnecessary misunderstanding and a rumor based conflict of the south-south. The progressive mainstream media mentioned much about Hwang Gyoan, external influences, and lies and highlighted the government's opinion that there was external influence that spread a vicious rumor. Conservative media mentioned on the bringing about social disturbance and in case of progressive media mentioned social disturbance, and progressive media mentioned the responsibility of government and the attitude of conservative media about the diffusion of the rumor. In conclusion the press framed the THAAD rumor on the basis of their ideological inclinations instead of the role of journalist.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.133-146
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• 2013

While the use of internet is routine nowadays, users receive and share information through a variety of media. Through the use of internet, information delivery media is diversifying from traditional media of one-way communication, such as newspaper, TV, and radio, into media of two-way communication. In contrast of traditional media, blogs enable individuals to directly upload and share news, which can be considered to have a differential speed of information diffusion than news media that convey information unilaterally. Therefore this Study focused on the difference between online news and social media blogs. Moreover, there are variations in the speed of information diffusion because that information closely related to one person boosts communications between individuals. We believe that users' standard of evaluation would change based on the types of information. As well, the speed of information diffusion would change based on the level of proximity. Therefore, the purpose of this study is to examine the differences in information diffusion based on the types of media. And then information is segmentalized and an examination is done to see how information diffusion differentiates based on the types of information. This study used the Bass diffusion model, which has been frequently used because this model has higher explanatory power than other models by explaining diffusion of market through innovation effect and imitation effect. Also this model has been applied a lot in other information diffusion related studies. The Bass diffusion model includes an innovation effect and an imitation effect. Innovation effect measures the early-stage impact, while the imitation effect measures the impact of word of mouth at the later stage. According to Mahajan et al. (2000), Innovation effect is emphasized by usefulness and ease-of-use, as well Imitation effect is emphasized by subjective norm and word-of-mouth. Also, according to Lee et al. (2011), Innovation effect is emphasized by mass communication. According to Moore and Benbasat (1996), Innovation effect is emphasized by relative advantage. Because Imitation effect is adopted by within-group influences and Innovation effects is adopted by product's or service's innovation. Therefore, ours study compared online news and social media blogs to examine the differences between media. We also choose different types of information including entertainment related information "Psy Gentelman", Current affair news "Earthquake in Sichuan, China", and product related information "Galaxy S4" in order to examine the variations on information diffusion. We considered that users' information proximity alters based on the types of information. Hence, we chose the three types of information mentioned above, which have different level of proximity from users' standpoint, in order to examine the flow of information diffusion. The first conclusion of this study is that different media has similar effect on information diffusion, even the types of media of information provider are different. Information diffusion has only been distinguished by a disparity between proximity of information. Second, information diffusions differ based on types of information. From the standpoint of users, product and entertainment related information has high imitation effect because of word of mouth. On the other hand, imitation effect dominates innovation effect on Current affair news. From the results of this study, the flow changes of information diffusion is examined and be applied to practical use. This study has some limitations, and those limitations would be able to provide opportunities and suggestions for future research. Presenting the difference of Information diffusion according to media and proximity has difficulties for generalization of theory due to small sample size. Therefore, if further studies adopt to a request for an increase of sample size and media diversity, difference of the information diffusion according to media type and information proximity could be understood more detailed.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.89.1-89.1
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• 2014

빛공해란, 불필요하거나 필요 이상의 인공빛이 야생 동식물들과 우리 인간들에게 악영향을 미치는 현상을 말하며, 실생활에서 인공빛 에너지를 목적에 맞지 않게 사용하는 것이 이 현상의 주요 원인이라고 할 수 있다. 빛공해 현상은 야생 동식물들에게 악영향을 주어 개체수를 감소시킬뿐만 아니라 멸종에까지 이르게 할 수 있으며, 지구 자전의 영향으로 하루 24시간 주기로 설정되어 있는 우리 인간의 생체리듬을 교란시켜 암, 비만, 당뇨병, 그리고 우울증 등과 같은 인간의 목숨을 위협할 수 있는 질병들을 일으키기도 한다. 하지만 인공빛을 목적에 맞게 올바르게 사용한다면 그로 인해 절약된 에너지와 그 비용을 다른 필요한 분야에 대체하여 사용할 수 있을 것이다. 우리는 과거의 빛공해 관련 논문과 보고서의 설문조사 결과를 통하여 빛공해로 인한 피해와 에너지 낭비 문제가 빛공해에 대한 일반 시민들의 무관심으로부터 발생하고 있다는 사실을 확인할 수 있었다. 따라서 우리는 빛공해에 대한 일반 시민들의 인식 변화를 알아보기 위하여 기존에 진행된 설문조사와 같은 문답내용의 설문조사를 올해 다시 실시하였다. 그 설문조사의 결과를 통하여 우리는 과거보다 빛공해에 대한 인식이 많이 확산되어 있다는 사실을 알 수 있었으며, 그 이유로는 최근 빛공해와 관련된 많은 뉴스 기사들과 함께 웹상의 소셜네트워크와 같은 다양한 경로의 정보매체들을 통하여 빛공해에 대한 정보를 보다 빠르고 쉽게 접할 수 있는 환경이 조성되었기 때문이라고 분석하였다. 빛공해 인식 확산에 더욱 더 기여하기 위하여 최근에 우리는 국제 어두운 밤하늘 협회 한국 지부 (Korean Chapter, International Dark-Sky Association) 인가를 받아 그 단체 이름으로 빛공해 방지 홍보 사업을 온라인과 오프라인을 통하여 보다 더 활발히 진행하기 위하여 많은 노력을 하고 있다.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.271-280
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• 2012

Currently, contents generated by SNS services are increasing exponentially, as the number of SNS users increase. The SNS is commonly used to post personal status and individual interests. Also, the SNS is applied in socialization, entertainment, product marketing, news sharing, and single person journalism. As SNS services became available on smart phones, the users of SNS services can generate and spread the social issues and controversies faster than the traditional media. The existing indexing methods for web contents have limitation in terms of real-time indexing for SNS contents, as they usually focus on diversity and accuracy of indexing. To overcome this problem, there are real-time indexing techniques based on RDBMSs. However, these techniques suffer from complex indexing procedures and reduced indexing targets. In this regard, we introduce the TK-Indexing method to improve the previous indexing techniques. Our method indexes the generation time of SNS contents and keywords by way of NoSQL to indexing SNS contents in real-time.