• Korean journal of communication and information
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• v.79
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• pp.165-195
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• 2016

This paper aims to examine the third-person effect(TPE) of hate comments on online news, and analyze how the issue-relevant audience factors as well as the characteristics of the online message have influence on the TPE. More specifically, based on the distinction between hateful and logical comments regarding the issue of illegal immigration, the authors have conducted an online experiment that compares how the message-related features, i.e., ways of expressing the ideas, lead to the difference in TPE. Analysis was also conducted with regards to how political orientation and discriminatory predisposition to immigrants among the audiences, have different impacts on the TPE. The 479 participants in the experiments were randomly assigned to experimental group(exposed to hate comments) or control group(exposed to logical comments). The results reveal that the TPE of hate comments is higher than that of logical message. The same message proved to be more effective for news users with liberal orientation and discriminatory predisposition. The significance of this paper lies in that it has examined the effect of online hate comments in a rigorous experimental setting. Also the research further elaborated on the audience-related variables, for which the previous studies tended to focus those on the general psychological level rather than relate them more specifically to the issues.

• Asian Journal for Public Opinion Research
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• v.11 no.2
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• pp.145-162
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• 2023

This research investigates how users of Bilibili, a video sharing website based in China have responded to carbon neutrality. By conducting quantitative textual analyses on 3,311 comments on Bilibili using LDA topic extraction and content statistics, this research discovers that: (1) Bilibili users have assigned more weight to geopolitical topics (56.3%) than energy (22.0%) and environmental topics (21.7%). (2) When assessing carbon neutrality, Bilibili users considered geopolitical (53.8%) and energy factors (15.8%) more heavily than factors related to the class (9.2%), economy (8.9%), environment (8.7%), and definition (3.6%). (3) More Bilibili users had negative (64.6%) attitudes towards carbon neutrality, with only a small portion of them expressing positive (26.8%) and neutral (8.6%) attitudes. (4) Negative attitudes towards carbon neutrality were mainly driven by geopolitical concerns about the West's approach to China, other countries' free-riding on China's efforts and the West's manipulation of rules, doubts about the feasibility of energy transition and suspicion of capitalists exploiting consumers through this concept. This research highlights the geopolitical concerns behind the environmental attitudes of Chinese people, deepening our understanding to psychological constructs and crisis sensitivity of Chinese people towards environmental issues.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• Journal of Information Technology Services
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• v.23 no.1
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• pp.55-79
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• 2024

Korea, which is expected to enter a super-aged society in 2025, is facing the most worrisome crisis worldwide. Efforts are urgently required to examine problems and countermeasures from various angles and to improve the shortcomings. In this regard, from a new viewpoint, we intend to derive useful implications by applying the recent natural language processing techniques to online articles. More specifically, we derive three research questions: First, what topics are being reported in the online media and what is the public's response to them? Second, what is the relationship between these aging-related topics and individual happiness factors? Third, what are the strategic directions and implications for benchmarking discussed to solve the problem of population aging? To find answers to these, we collect Naver portal articles related to population aging and their classification categories, comments, and number of comments, including other numerical data. From the data, we firstly derive 33 topics with a semi-supervised BERTopic by reflecting article classification information that was not used in previous studies, conducting sentiment analysis of comments on them with a current open-source large language model. We also examine the relationship between the derived topics and personal happiness factors extended to Alderfer's ERG dimension, carrying out additional 3~4-gram keyword frequency analysis, trend analysis, text network analysis based on 3~4-gram keywords, etc. Through this multifaceted approach, we present diverse fresh insights from practical and theoretical perspectives.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.45-56
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• 2009

There are many internet users who collect the public opinions and express their opinions for internet news or blog articles through the replying comment on online community. But, it is hard to search and explore useful messages on web blogs since most of web blog systems show articles and their comments to the form of sequential list. Also, spam and malicious comments have become social problems as the internet users increase. In this paper, we propose a clustering and visualizing system for responding comments on large-scale weblogs, namely 'Daum AGORA,' using similarity analysis. Our system shows the comment clustering result as a simple screen view. Our system also detects spam comments using Needleman-Wunsch algorithm that is a well-known algorithm in bioinformatics.

• The Korean Journal of Pain
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• v.35 no.1
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• pp.124-125
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• 2022

• Journal of Information Technology Services
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• v.23 no.3
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• pp.103-118
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• 2024

Due to the advancement of virtual reality technology, virtual idols are widely used in industrial and cultural content industries. However, it is difficult to utilize virtual idols' social perceptions because they are not properly understood. Therefore, this paper collected and analyzed YouTube comments to identify differences about social perception through comparative analysis between virtual idols and general idols. The dataset was constructed by crawling comments from music videos with more than 10 million views of virtual idols and more than 10,000 comments. Keyword frequency and TF-IDF values were derived from the collected dataset, and the connection centrality CONCOR cluster was analyzed with a semantic network using the UCINET program. As a result of the analysis, it was found that virtual idols frequently used keywords such as "person," "quality," "character," "reality," "animation," while reactions and perceptions were derived from general idols. Based on the results of this analysis, it was found that while general idols are mainly evaluated with their appearance and cultural factors, social perceptions of virtual idols' values are mixed with evaluations of cultural factors such as "song," "voice," and "choreography," focusing on technical factors such as "people," "quality," "character," and "animation." However, keywords such as "song," "voice," "choreography," and "music" are included in the top 30 like regular idols and appear in the same cluster, suggesting that virtual idols are gradually shifting away from minority tastes to mainstream culture. This study aims to provide academic and practical implications for the future expansion of the industry and cultural content industry of virtual idols by grasping the social perception of virtual idols.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.92-101
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• 2017

The SNS website can take full advantage of the characteristics of users to conduct e-commerce. The e-commerce website's organizing ability will be greatly strengthened by SNS and creates greater value for consumers. This article examined the Chinese largest SNS (Weibo) users as research objects, and combined the development status of SNS in China. This article focuses on the influence to consumer's purchase intention in three aspects: number of comments, consumer involvement level, and consumer appealing method and examines how the interaction of the number of comments and consumer appealing method affects the purchase intention. An investigation was conducted on 400 users of SNS and using valid questionnaires to perform reliability analysis, validity analysis, independent sample t-test, and double factor variance analysis using SPSS21. The research results indicated that the number of comments and rational appealing method had significant effect on the purchase intention. The mediating or controlling the purchase involvement level will disturb the influence of the number of comments but will have no effect on the information appealing method.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.119-133
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• 2020

Recently, as famous YouTubers produce and broadcast videos that receive sponsorship and advertising such as indirect advertising (PPL), a so-called 'back advertising' controversy continues, and not only famous YouTubers but also entertainers are caught up in the issue. It is causing confusion among the public in Korea. This study attempts to find out the public's reaction before and after the controversy of 'back advertising' by YouTubers through comment analysis. Specifically, among text analysis using R programs, we intend to analyze the issue through various methods such as word cloud, qgraph analysis, LDA, and word2vec analysis, a deep learning technique. The target of the analysis was to analyze the channels of three YouTubers who belonged to the controversy of the 'back advertising' YouTuber and uploaded the 'Apology video'. The 5 most recent videos of Muk-bang YouTuber Moon Bok-hee, who has a similar content disposition to SussTV's Han Hye-yeon stylist, which was controversial, and Yang Pang, a YouTuber who showed various contents (August 09, 2020) Criterion and her first 5 videos uploaded were reviewed. As a result of the study, most of the comments that showed positive reactions before the controversy, but after the controversy, it was found that negative reactions accounted for most of the comments. Therefore, this study examines the degree of change of the public about influencers through comments after the controversy over 'back advertising' through various analysis using R program. This research also devises various measures to prevent the occurrence of back advertising of influencers in the future.

• Journal of Elementary Mathematics Education in Korea
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• v.19 no.4
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• pp.625-647
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• 2015

How a pre-service teacher understands and comments on mathematics instruction can serve as the foundation of her teaching expertise. Given that prospective teachers observe demonstrative mathematics teaching implemented by an in-service teacher and make a comment on it during their practicum period, this paper specified the levels of their ability in commenting on mathematics instruction and explored the characteristics of such levels. It is significant that this paper provides a systematic and comprehensive analysis of such levels in terms of topic, agent, stance, evidence, and alternative perspective. The results of this study showed that the commenting levels may be classified by Level 1 (fragmentary), Level 2 (inspective), and Level 3 (analytical), and that the most frequent level of this study was at Level 2. Multiple regression analysis demonstrated that stance is the most influential in determining the levels of comments among their analytic components. An analysis of the participants' anecdotes showed that the experience of observing demonstrative teaching during the practicum may have impact on the belief of mathematics instruction and self-image as a teacher. Building on these results, this paper provides implications of teacher preparation programs to enhance prospective teachers' ability to analyze elementary mathematics lessons.