• The Journal of Information Systems
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• v.30 no.4
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• pp.303-327
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• 2021

Purpose The online comments on news content have become social information and are understood based on deliberative democracy. Although the related research has focused on the relationship between online comments and their deliberative quality, the social information provided by online comments consists of not only direct information such as comments themselves but also indirect information such as 'likes' and 'dislikes'. Therefore, the research on online comments and deliberative quality should study this direct and indirect information together, and the direction and the degree of the indirect information should be also considered with them. Design/methodology/approach This study distinguishes comments by the attached 'likes' and 'dislikes', identifies highly supported and highly unsupported comments by the intensity of 'likes' and 'dislikes', and investigates the relationship between their existence and the deliberative quality measured as the topic diversity. Then, we applied topic modeling to the 2,390 news articles and their 74,385 comments collected from five news sites. Findings The topic diversities of the supported and unsupported comments are related to the topic diversity of all comments but the degree of the relationship is higher in the case of supported comments. Furthermore, the existence of highly supported and unsupported comments is led to less diversity of all comments compared to the case where those comments are absent. Particularly, when only highly supported comments are present, topic diversity was lower than in the opposite case.

• Knowledge Management Research
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• v.17 no.4
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• pp.191-213
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• 2016

Since the smartphone era has spurred world-over, social network services have become a part of people's daily lives. However, this relatively new phenomenon of technology development raises several negative side effects such as cyberbullying. One of the representative cases of cyberbullying is posting malicious comments online. Multiple social issues arising from this have given impetus to the "benevolent comments campaign" in order to restrain the diffusion of malicious comments. Benevolent comments have advantages that generate positive externalities such as inspiring ethics for an appropriate internet culture, but there is a lack of theoretical research on the deeper understanding of posting benevolent comments. This study thus aims to extract the motivations behind posting benevolent comments through in-depth interviews and suggest alternatives for relative issues through the causal relationship diagram of the system dynamics methodology. This work contributes to our understanding of the factors that affect the increase and decrease in benevolent comments in distinct structural frameworks.

• Journal of Information Technology Applications and Management
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• v.25 no.1
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• pp.87-104
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• 2018

The internet is spreading widely and malicious comments which is a negative aspect is increasing. Previous studies have considered anonymity as a cyber characteristic of malicious comments. However, there are a theoretical confusion due to inconsistent results. In addition, the dissemination, one of cyber characteristics, have been mentioned the theoretical relationship on malicious comments, but measurement and empirical study about dissemination were still limited. Therefore, this study developed a measurement of dissemination and investigated the relationship between cyber characteristics (anonymity, dissemination) and malicious comments on Facebook. As a result of research, this study identified that anonymity is not significant on malicious comments and discovered that the dissemination of cyber space has a direct influence on malicious comments. This study suggests that information systems can contribute to malicious comments researches by proposing cyber characteristics.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.159-176
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• 2015

As a public medium and one of the big data sources that is accumulated informally and real time, online news comments or replies are considered a significant resource to understand mentalities of article readers. The comments are also being regarded as an important medium of WOM (Word of Mouse) about products, services or the enterprises. If the diffusing effect of the comments is referred to as the degrees of agreement and disagreement from an angle of WOM, figuring out which characteristics of the comments would influence the agreements or the disagreements to the comments in very early stage would be very worthwhile to establish a comment-based eWOM (electronic WOM) strategy. However, investigating the effects of the characteristics of the comments on eWOM effect has been rarely studied. According to this angle, this study aims to conduct an empirical analysis which understands the characteristics of comments that affect the numbers of agreement and disagreement, as eWOM performance, to particular news articles which address a specific product, service or enterprise per se. While extant literature has focused on the quantitative attributes of the comments which are collected by manually, this paper used text mining techniques to acquire the qualitative attributes of the comments in an automatic and cost effective manner.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.750-761
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• 2021

Even though the replying comments on Internet articles have positive effects on discussions and communications, the malicious comments are still the source of problems even driving people to death. Automatic detection of malicious comments is important in this respect. However, the current filtering method of the malicious comments, based on forbidden words, is not so effective, especially for the replying comments written in Korean. This paper proposes a new filtering approach based on morpheme analysis, identifying coarse and polite morphemes. Based on these two groups of morphemes, the soundness of comments can be calculated. Further, this paper proposes various impact measures for comments, based on the soundness. According to the experiments on malicious comments, one of the impact measures is effective for detecting malicious comments. Comparing our method with the clean-bot of a portal site, the recall is enhanced by 37.93% point and F-measure is also enhanced up to 47.66 points. According to this result, it is highly expected that the new filtering method based on morpheme analysis can be a promising alternative to those based on forbidden words.

• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1489-1497
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• 2018

The importance of comments is constantly growing as a participation of individual in Online News that being invigorated. The 'Best Comments', which strongly related by major participants are recognized as a primary public opinion, and obtains the power. Thus this study is aimed to analyze the characteristics of the 'Best Comments' by utilizing the data of comments on Online News. For this study, a possible element that may reveal the difference between 'general' comments and 'best' comments were set up, digitalized the data, and examined the difference between 'general' and 'best' comments. This study is expected to provide a clue for the problematic issues, such as 'online comment rigged scandal' in recent; also as a basic data that subjected by the individual, academic society, government, and etc.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.548-558
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• 2019

Along generalization of internet news comments, malicious comments have been spread and made many social problems. Because writings reflect human mental state or trait, analyzing malicious comments, human mental states could be inferred when they write internet news comments. In this study, we analyzed malicious comments of English and Korean speaker using LIWC and KLIWC. As a result, in both English and Korean, malicious comments are commonly more used in sentence, word phrase, morpheme, word phrase per sentence, morpheme per sentence, positive emotion words, and cognitive process words than normal comments, and less used in the third person singular, adjective, anger words, and emotional process words than normal comments. This means people are state that they can not control their feeling such as anger and can not think well when they write news comments. Therefore, when internet comments were written, service provider should consider the way that commenters monitor own writings by themselves and that they prevent the other users from getting close to comments included many negative-emotion words. In other sides, it is discovered that English and Korean malicious comments was discriminated by authenticity. In order to be more objective, gathering data from various point of time is needed.

• Information Systems Review
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• v.22 no.4
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• pp.163-184
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• 2020

The information on SNS, which is created by the forms of postings and comments, is being magnified and redistributed to news media expanding its impacts on real words. This amplifying effects of SNS comments have been increasingly discussed but there still lacks the answers for which dimensions of information is magnified, and what affects the direction and the degree of the amplification. This study, therefore, explores the detailed dimensions that are magnified by SNS comments and how SNS posting structure and social network characteristics affect them by using sentiment analysis. By analyzing 2,378 Facebook postings and news articles and their 26,312 SNS and 74,730 news site comments, this study shows that SNS comments magnify the sentiments of the posting articles they are attached to. In comparison to news site comments, SNS comments magnify the cognitive and social dimensions more than the news site comments. In the affective dimension, they tend to magnify only the positive emotion more than news site comments. Also, the findings reveal that whether the article in the posting is written by the posting owner affects the degree of amplification when the comments are remained positive or switched positive, while the opposite determines the amplification when comments remain negatively, suggesting that the user relationship in social network is the important factor that affects the direction and the degree of the information amplification in SNS.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.12
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• pp.817-826
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• 2018

As malicious comments are emerging as social problems, the solution is needed. Many studies have been conducted in various perspectives to understand and prevent malicious comments. In the previous researches, the neutralization of malicious comments has attracted attention as an important factor explaining the malicious comments, but the difference of the degree of neutralization according to the gender has not been rarely considered. In addition, although there are many environmental characteristics that are different from reality in online, research with malicious comments is insufficient. Based on these facts, this study examined moderating effects of gender on relationship between malicious comments and neutralization, and demonstrated the effects of online environmental factors (anonymity, lack of social presence) on malicious comments. As a result of the study, we discovered that the influence of online environmental factors was not found, but neutralization of malicious comments had strong direct influence on malicious comments and moderating effect of gender difference. Based on the results of this study, we discuss academic and practical implications and suggest limitations of research and future research directions.

• The Journal of Information Systems
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• v.29 no.3
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• pp.103-117
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• 2020

Purpose Recently, various issues related to toxic comments on web portal sites and SNS are becoming a major social problem. Toxic comments can threaten Internet users in the type of defamation, personal attacks, and invasion of privacy. Over past few years, academia and industry have been conducting research in various ways to solve this problem. The purpose of this study is to develop the deep learning modeling for toxic comments classification. Design/methodology/approach This study analyzed 7,878 internet news comments through CNN classification modeling based on Highway Network and OOV process. Findings The bias and hate expressions of toxic comments were classified into three classes, and achieved 67.49% of the weighted f1 score. In terms of weighted f1 score performance level, this was superior to approximate 50~60% of the previous studies.