• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.1-10
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• 2019

In terms of realizing the future e-government such as intelligent government, this paper attempts to provide an earnest and insightful reflection and suggests desirable strategies with regard to the four different crucial elements including electronic voting, electronic contract, resident registration/electronic document management, and real-estate registration as an operating strategy of intelligent government and the fourth industrial revolution regarding. The 4th industrial revolution is aimed at concentrating information or data characterized with sharing, opening, communicating and releasing in cloud computing system, analyzing big data, collecting information, and flourishing people's well-being by information and communications technology with utilizing the smart devices. Therefore, reliability of the pivotal information or data is critical and it is important for the participants being transparently shared, without the data or information being forged. In this respect, introduction or application of block chain technology is essential. This paper will review preceding studies, discuss the aspect of the 4th industrial revolution and intelligent government, then suggest operating strategies in the field of electronic voting, electronic contract, management of resident registration and electronic document and real-estate registration.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.105-118
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• 2021

This study pays attention to the fact that the fake news is attracting attention because it causes various social problems. To find out these fake news' influence, the study conducted the experiment to examine that the fake news related to the electronic voting system affects on the news users' attitude of using the system, intention to participate in the election through the system and reliability of news services. The results have shown that the fake news framed with negative contents reduced users' attitude of using the system and intention of participation in the election. Especially, as a result of examining the difference in the fake news' influence according to each news services, in the case that users recognized that the news was fake after exposing to the general internet news, the attitude of using the system and the intention of participation in the election have reduced and recovered again. However, users who exposed to Naver, Facebook believed the negative content of the fake news more strongly. Through these results, this study empirically confirmed that the fake news has a tendency to exert influence on users' cognitive dimension and to reinforce awareness in a direction consistent with the initial exposure information.

• The Journal of Korean Association of Computer Education
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• v.11 no.1
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• pp.97-107
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• 2008

The result verification that determines correctness for the work results calculated in each PC is one of the most important issues in PC grid environments. In this literature, voting-based and trust-based schemes have been mainly used to guarantee the correctness of work results. However, these schemes suffer from both waste of resource utilization and high computation delay because they can not effectively cope with dynamic computational environments. To overcome these shortcomings, we introduce the distribution information of PC resources based on credibility and availability into result verification phase. Using this information, we propose a new result verification scheme, which can determine the correctness of work results by each PC resources' credibility and cope with the dynamic changing environments by each PC resources' availability. To demonstrate the efficiency of our result verification scheme, we evaluate the performance of our scheme from the viewpoints of turnaround time and resource utilization, utilizing resource distribution information in the Korea@Home that is a representative PC grid system in domestic. We also compare the performance of our scheme with that of other ones.

• Korean journal of communication and information
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• v.42
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• pp.79-106
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• 2008

Based on the third-person effect hypothesis, this study conducted a nation-wide online survey to assess how Korean voters perceived the mass media's effect on the candidates' image and voting behavior during the 17th presidential election. The research results showed that the voters tended to perceive that the mass media such as newspaper, television and the Internet had a greater effect on others than on themselves with regards to the formation of the three candidates' images. The third-person effect on the voting behavior was also revealed differently in terms of the medium according to age and political tendency of the voters. For instance, the younger and liberal voters were likely to see newspaper as having a greater influence on other voters' choice of candidate, while the older voters saw TV as having a greater effect on other voters. The conservative tendency did not affect the perception of the voters at all. Another noteworthy result was that personal characteristics of the candidates' images such as appearances and communication skills did not affect the voters' behaviors in the election process.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.207-214
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• 2013

It was the 18th presidential election TV debate Twitter participation of SNS. Began to diverge as the era of social media, combined with SNS through in the mass media, media web 2.0. Search tweets, retweets, while the formation of policy issues, the agenda of Twitter users to listen to the statements of the candidates using the Internet or a smartphone. The highest number of tweets immediately issue statements were made. Content during the progressive tweets core keywords you do not often discussed, followed by the negative information increases the number of tweets has become a policy issue. Top retweets was to evaluate the process of debate, regardless of the issue. Tweeter complements the TV so Twitter has made public opinion. Smart phones and SNS Twitter, combined with the TV and the participation and direct democracy, voters vote one instrument was realized. Should forward approval ratings, real-time Twitter subtitles on the TV screen in TV debate Twitter influence in the election will be greatly expanded.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of Korean Library and Information Science Society
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• v.48 no.3
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• pp.113-130
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• 2017

The goal of this study is to propose the approaches to improve the voters' awareness by analyzing the voters' awareness on the fake news related to the elections and identifying the problems with the focus on the 19th Presidential Election. In accordance with the analysis on the data from 128 respondents (53 male and 75 female respondents), the 99.2% (127 respondents) of respondents had informations on elections mainly through broadcasting(77.2%), smart phone(70.9%), Internet(63.8%) and newspapers 32.3% which accounts for 41 respondents) in that sequence. Next, the 87.4% of respondents thought that the informations on elections had more impact on their voting than the generally expected degree. Meanwhile, the voters' awareness on the facts was analyzed by collecting and presenting the information on elections which stated by candidates in the 19th Presidential Election. In accordance with the analysis, there were the significant differences per age groups. The Scheffe test indicated that the respondents in 30s to 40s had significantly higher average awareness than those in 20s. According to the analysis results, it was proposed that the National Election Commission install the election information investigation and analysis committee in the election organization, investigate and analyze the election informations each election for providing real facts to the public, the voters.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.