• Journal of Contemporary Eastern Asia
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• v.16 no.1
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• pp.48-68
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• 2017

Manual elections processes in Egypt have several negative effects; that mainly leads to political corruption due to the lack of transparency. These issues negatively influence citizen's participation in the political life; while electronic voting systems aim to increase efficiency, transparency, and reduce the cost comparing to the manual voting. The main research objectives are, finding the successful factors that positively affects E-voting implementation in Egypt, in addition of finding out the reasons that keep Egyptian government far from applying E-voting, and to come up with the road map that Egyptian government has to take into consideration to successfully implement E-voting systems. The findings of the study suggest that there are seven independent variables affecting e-voting implementation which are; leadership, government willingness, legal framework, technical quality, awareness, citizen's trust in government and IT literacy. Technology-Organization-Environment (TOE) theory was used to provide an analytical framework for the study. A quantitative approach (i.e., survey questionnaire) strategy was used to collect data. A random sampling method was used to select the participants for the survey, whom are targeted voters in Egypt and have access to the internet, since the questionnaire was distributed online and the data is analyzed using regression analysis. Practical implications of this study will lead for more citizen participation in the political life due to the transparency that E-voting system will create, in addition to reduce the political corruption.

• Asian Journal for Public Opinion Research
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• v.11 no.2
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• pp.107-144
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• 2023

In competitive political contexts, sustaining power is the ultimate goal for political parties. Nominating celebrity politicians can be a double-edged sword for parent brands in attracting votes and influencing voting intention. This study contributes to the moderating role of brand affinity towards celebrity politicians. It considers celebrities' cognitive perceived benefits and voting intention relationship in a multiparty parliamentarian election. A cross-sectional survey with a stratified proportional random sampling technique in fifteen Lebanese districts ensured a representative sample. One thousand two hundred sixty-nine responses were found eligible for analysis. Findings indicate that brand affinity positively moderates the negative relationship between perceived benefits and voting intention. This study offers a new understanding of celebrity politicians' implementation strategy and campaign management and considers the contribution of the affective intelligence theory. It provides implications, limitations, and promising directions for future research on celebrity politicians.

• Asian Journal for Public Opinion Research
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• v.2 no.4
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• pp.248-278
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• 2015

Despite the proliferation of studies on emotion, little attention has been paid to the effects of discrete emotion on political participation. Using a representative survey conducted on a sample of South Korean citizens in the aftermath of the Sewol ferry accident, the current survey explored how anger and sadness, as well as the ways people express those emotions, influence the orientation of their response in social environments and, ultimately, their voting intention. The results partially supported the discrete effects of sadness and anger in eliciting reactions of approach or avoidance. Anger was found to provoke an approach action tendency in independent voters and supporters of the opposition, while also eliciting an avoidance action tendency with a varying effect size across all three groups of respondents. Sadness also prompted an approach action tendency in independents and supporters of the incumbent party, while it manifested a negative association with the avoidance action tendency in supporters of the opponent party. An interpretation of the findings and proposed directions for future research are presented.

• 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 Internet Computing and Services
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• v.5 no.1
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• pp.51-57
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• 2004

Mobile Internet allows users to request critical information and receive swift responses at any places, but mobile users could suffer from unreliable and ill-timed services due to the characteristics of wireless media, One way that reduces possibility of the unsatisfactory services is data replication. Data Replica1ion, however, inevitably induces the overhead of maintaining replica consistency which requires more expensive synchronization mechanism. We propose a new replicated data management scheme in distributed mobile environment, In order to alleviate negative impact of synchronization message overhead in fault-prone mobile Internet environment, we devise a new replication control scheme called proxy quorum consensus (PQC), PQC minimizes the message overhead by coordinating quorum access activities by means of proxy mediated voting (PMV) which exploits reliable proxy hosts instead of unreliable mobile hosts in voting process, We also propose a simulation model to show the performance of PQC. Based on the results of the performance evaluation, we conclude that PQC scheme outperforms the traditional schemes.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.427-432
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• 2019

In this paper, we propose three approaches to modeling Android malware. The first method involves human security experts for meticulously selecting feature sets. With the second approach, we choose 300 features with the highest importance among the top 99% features in terms of occurrence rate. The third approach is to combine multiple models and identify malware through weighted voting. In addition, we applied a novel method of eliminating permission information which used to be regarded as a critical factor for distinguishing malware. With our carefully generated feature sets and the weighted voting by the ensemble algorithm, we were able to reach the highest malware detection accuracy of 97.8%. We also verified that discarding the permission information lead to the improvement in terms of false positive and false negative rates.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.481-488
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• 2007

In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several approaches to classify documents. The previous studies, however, used only a single classifier for the classification task. We describe a multiple classifier for the review document classification task. The method consists of three classifiers based on SVMs, ME and score calculation. We apply two voting methods and SVMs to the integration process of single classifiers. The integrated methods improved the accuracy as compared with the three single classifiers. The experimental results show the effectiveness of our method.

• Journal of Information Science Theory and Practice
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• v.2 no.3
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• pp.18-28
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• 2014

Reddit is a large user generated content (USG) website in which users form common interest groups and submit links to external content or text posts of user-created content. The web site operates on a voting system whereby registered users can assign positive or negative ratings to both submitted content and comments made to submitted content. While Reddit is a pseudonymous site, with users creating usernames but providing no biographical data, an informal survey posted to a large shared interest community yielded 734 responses including age and gender of users. This provided a large amount of contextual biographical data with which to analyse user profiles at the first level of Computer Mediated Discourse Analysis (CMDA), articulated by Susan Herring. The results indicate that older Reddit users both formulate more complex writing and enjoy more success when rated by other users. Gender data was incomplete and as such only tentative results could be proposed in that regard.

• Korean Journal of Legislative Studies
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• v.27 no.2
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• pp.5-40
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• 2021

In the 4.7 by-election in 2021, the ruling Democratic Party suffered a record devastating defeat, breaking the trend of a post-intermediate evaluation confirmed in the recent election. Why did the Democratic Party lose by a large margin unlike the recent election trend? In order to find answers to these questions, this study analyzes the voting behavior of individual voters based on the voter consciousness survey data conducted after the 4.7 by-election, while examining the background and causes of such voter choices. As a result of the study, in the 4.7 by-election, as confirmed in previous studies, public opinion against the ruling government was strong, and negative elections were held. However, if we look at the process and results of this by-election in more detail, we can see that it is different from the general by-election. In the past by-elections, the government judgement was due to the passive participation of the ruling party-oriented voters in elections with low political weight, or the active judgement psychology that was maximized in situations where the political burden was less. However, in this by-election, on the contrary, in an election with a high political weight, the active judgement psychology of the Democratic Party and non-partisan voters had an effect on strengthening the midterm evaluation character of the election. In addition, it can be seen that the gathering of conservative voters who support the opposition also had a strong influence on the reinforcement of the midterm evaluation character of the election.