• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.29-39
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• 2020

Online communities, such as Internet portal sites and social media, have become popular since they allow users to share opinions and to obtain information anytime, anywhere. Accordingly, an increasing number of opinions are manipulated to the advantage of particular groups or individuals, and these opinions include falsified product reviews and political propaganda. Existing detection systems are built upon the characteristics of manipulated opinions for one particular time period. However, manipulation tactics change over time to evade detection systems and to more efficiently spread information, so detection systems should also evolve according to the changes. We therefore propose a system that helps observe and trace changes in manipulation tactics. This system classifies opinions into clusters that represent different tactics, and changes in these clusters reveal evolving tactics. We evaluated the system with over a million opinions collected during three election campaigns and found various changes in (i) the times when manipulations frequently occur, (ii) the methods to manipulate recommendation counts, and (iii) the use of multiple user IDs. We suggest that the operators of online communities perform regular audits with the proposed system to identify evolutions and to adjust detection systems.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.28-37
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• 2020

This study was conducted to propose measures to improve crisis response to environmental issues by analyzing the news big data on the 'tap water larvae' situation and identifying related major keywords and topics. To accomplish this, 1,975 cases of 'tap water larvae' reported between July 13 to August 31, 2020 were divided into three periods and analyzed using topical modeling techniques. The analysis output 15 topics for each period. According to the result, the 'tap water larvae' incident, as reported in the media, is divided into the occurrence, diffusion, and rectification stages. The government's response and civilian risk consciousness and reaction could also be seen. Based on the result, the following measures to respond to environment risk is proposed. First, it is necessary to explore the various intertwined context with the 'tap water larvae' incident at its core and develop responsiveness to environmental problems through education which forms integrated views. Second, a role to monitor the environment must be implemented and civilian-participated environmental information must be shared through the application of internet communities. Third, the cultivation and deployment of environmental communicators who provide and communicate fast and accurate environment information is required. This study, as the first in Korea to use the topic modeling analysis method based on big data related to 'tap water larvae', has academic significance in that it has empirically and systematically analyzed environmental issues which appear as unstructured data. It also political significance as it suggests ways to improve environmental education and communication.

• Korean journal of communication and information
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• v.63
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• pp.53-75
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• 2013

This study looks into Dooriban movement, which problematizes the ways in which social movements are carried out in everyday settings in modern Korea. Contrary to traditional social movements, Dooriban movement led by active/independent participants -who are locally situated and culturally sensitized- showed a new way of engaging with political power. Making use of qualitative methodology such as in-depth interviews and participant observation, this research examines the implications of emergent cultural practices that redefine and reconfigure the working mechanisms of social movements in Korea today.

• Korean journal of communication and information
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• v.32
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• pp.49-73
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• 2006

During the past decades, public opinion polls have become an ubiquitous tool for probing the complexity of people's beliefs and attitudes on a wide variety of issues. Especially since the late 1970s, the use of polls by news organizations has increased dramatically. Along with the proliferation of traditional polls, in the past few years pollsters and news organizations have come to recognize the advantages of online polls. Increasingly there has been more effort to take the pulse of the public through the Internet. With the Internet's rapid growth during the past years, advocates of online polling often emphasize the relative advantages over traditional polls. Researchers from Harris Black International Ltd., for example, argue that "Internet polling is less expensive and faster and offers higher response rates than telephone surveys." Moreover, since many of the newer online polls draw respondents from large databases of registered Internet users, results of online polls have become more balanced. A series of Harris Black online polls conducted during the 1998 gubernatorial and senatorial elections, for example, has accurately projected the winners in 21 of the 22 races it tracked. Many researchers, however, severely criticize online polls for not being representative of the larger population. Despite the often enormous number of participants, Internet users who participate in online polls tend to be younger, better educated and more affluent than the general population. As Traugott pointed out, the people polled in Internet surveys are a "self selected" group, and thus "have volunteered to be part of the test sample, which could mean they are more comfortable with technology, more informed about news and events ... than Americans who aren't online." The fact that users of online polls are self selected and demographically very different from Americans who have no access to the Internet is likely to influence the estimates of what the majority of people think about social or political issues. One of the goals of this study is therefore to analyze whether people perceive traditional and online public opinion polls differently. While most people might not differentiate sufficiently between traditional random sample polls and non representative online polls, some audiences might perceive online polls as more useful and representative. Since most online polls allow some form of direct participation, mostly in the form of an instant vote by mouse click, and often present their findings based on huge numbers of respondents, consumers of these polls might perceive them as more accurate, representative or reliable than traditional random sample polls. If that is true, perceptions of public opinion in society could be significantly distorted for those who rely on or participate in online polls. In addition to investigating how people perceive random sample and online polls, this study focuses on the perceived impact of public opinion polls. Similar to these past studies, which focused on how public opinion polls can influence the perception of mass opinion, this study will analyze how people perceive the effects of polls on themselves and other people. This interest springs from prior studies of the "third person effect," which have found that people often tend to perceive that persuasive communications exert a stronger influence on others than on themselves. While most studies concerned with the political effects of public opinion polls show that exit polls and early reporting of election returns have only weak or no effects on the outcome of election campaigns, some empirical findings suggest that exposure to polls can move people's opinions both toward and away from perceived majority opinion. Thus, if people indeed believe that polls influence others more than themselves, perceptions of majority opinion could be significantly altered because people might anticipate that others will react more strongly to poll results.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."