• 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.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.817-824
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• 2009

In recent years, Weblog has become the most typical social media for citizens to share their opinions. And, many Weblogs reflect several social issues. There are many internet users who actively express their opinions for internet news or Weblog articles through the replying comments on online community. Hence, we can easily find internet blogs including more than 10 thousand replying comments. It is hard to search and explore useful messages on weblogs since most of weblog systems show articles and their comments to the form of sequential list. In this paper, we propose a visualizing and clustering system called TRIB (Telescope for Responding comments for Internet Blog) for a large set of responding comments for a Weblog article. TRIB clusters and visualizes the replying comments considering their contents using pre-defined user dictionary. Also, TRIB provides various personalized views considering the interests of users. To show the usefulness of TRIB, we conducted some experiments, concerning the clustering and visualizing capabilities of TRIB, with articles that have more than 1,000 comments.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.33-55
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• 2022

This study analyzed the comments and the replies on internet news related to the presidential election in order to verify whether online discussions are properly conducted. According to Habermas' public sphere theory, discussions is an effort among participants to reach a social consensus through the deliberations that are based on open communications. We propose that if such discussions properly take place through the act of writing in the Internet space, the comments and the replies will show a certain difference in terms of the structure and the content. To validate, this study analyzed more than 40,000 comments collected from Daum News portal site in Korea. The topic of the related news was the presidential election, because it is a topic of which people are highly interested in and that comments are actively running. The result of the t-test and topic modeling result show that all the hypotheses were supported thus we conclude that online discussions properly took places. This study also showed that online comments are not chaotic remarks that relieve people's stresses, but rather an outcome of the deliberation processes moving towards a social consensus.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.61-79
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• 2022

This study investigated the effect of cognitive dissonance that people experience during online communication on face-to-face communication. When people communicate with others on Interne using the comment system, they know that all the people including themselves equally participate in the discussion as one of the many commenters. At the same time, they somewhat distrust other commenters' attitudes because of the Internet anonymity. We name this seemingly contradicting beliefs as cognitive dissonance and examine how these beliefs affect the intention to communicate in face-to-face. Also, the proposed research model includes other factors such as curiosity and the differences in attitudes between online and offline. To verify the hypotheses, a total of 323 comment system users were recruited and show that most of the hypotheses were supported. This study emphasized its implications by examining the reasons when and why people prefer direct communication rather than comment based communication.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.23-28
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• 2020

A comment system is essential for communication on the Internet. However, there are also malicious comments such as inappropriate expression of others by exploiting anonymity online. In order to protect users from malicious comments, classification of malicious / normal comments is necessary, and this can be implemented as text classification. Text classification is one of the important topics in natural language processing, and studies using pre-trained models such as BERT and graph structures such as GCN and GAT have been actively conducted. In this study, we implemented a comment classification system using BERT, GCN, and GAT for actual published comments and compared the performance. In this study, the system using the graph-based model showed higher performance than the BERT.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.38-50
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• 2010

In recent years, the number of users who are actively express their opinions about Internet articles is more and more growing up, as the use of cyber community such as weblog or Internet discussion board increases. In fact, it is not difficult to find an article with hundreds of comments in famous Internet discussion boards. Most of the weblogs or Internet discussion boards present comments in the form of list and do not yet support even the basic operation such as searching comments. In this paper, we analysed large sets of comments in Internet discussion board named AGORA. It was found that from the result that the distribution of comment writers follows power-law. So we suppose a new search structure of comments using skip lists. The main idea of our approach is to reflect the probabilistic distribution properties of the commenters following the power-law to the data structure. Our empirical results show that the proposed method performs more efficient in searching the nodes with fewer number of comparison operations than logN, which is the theoretical time complexity of general indexed structure such as B-trees or typical skip lists.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.775-778
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• 2021

악성 댓글은 언어폭력이며 사이버 범죄의 일종으로 인터넷상에서 상대방이 올린 글에 비방이나 험담을 하는 악의적인 댓글을 말한다. 악성 댓글을 단순히 차단하는 다른 프로그램들과는 달리 해당 영상의 악성 댓글의 비율을 알려주고 악플러들의 닉네임과 그 빈도를 나타내주는 것으로 차별화를 두었다. 따라서 많은 유튜버들이 겪는 악성 댓글 문제들을 탐지하여 유튜브에 달리는 악성 댓글들을 탐지하고 시각화하여 제공한다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.447-449
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• 2012

인터넷이 보급화 됨에 따라 사용자들이 온라인 커뮤니티에 댓글을 다는 것으로 자신의 의견을 적극적으로 나타내려는 추세가 심화되고 있다. 일부 활성화 되어있는 인터넷 커뮤니티에서는 수천 수만개의 댓글이 달린 게시물도 찾아볼 수 있다. 본 논문에서는 이러한 게시물들이 나타내는 댓글이 형성하는 구조에 대해 트리구조로 정의하고 이러한 댓글 트리의 단일 성분이 어떠한 분포를 나타내는지 알아보고자 한다.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.6
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• pp.871-879
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• 2010

In this paper, we present a system that classifies comments on a news article into a user opinion called a polarity (positive or negative). The system is a kind of document classification system for comments and is based on machine learning techniques like support vector machine. Unlike normal documents, comments have their body that can influence classifying their opinions as polarities. In this paper, we propose a feature weighting scheme using such characteristics of comments and several resources for opinion classification. Through our experiments, the weighting scheme have turned out to be useful for opinion classification in comments on Korean news articles. Also Korean character n-grams (bigram or trigram) have been revealed to be helpful for opinion classification in comments including lots of Internet words or typos. In the future, we will apply this scheme to opinion analysis of comments of product reviews as well as news articles.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.576-583
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• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.