• Journal of Digital Convergence
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• v.20 no.5
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• pp.733-739
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• 2022

The study investigated the users' functional requirement, degree of acceptance and preference of game broadcast real-time review .Secondly it comparatively analyzed the types and locations of game broadcast real-time review through eye tracking tech. The results show that users have high functional requirements and high acceptance for game broadcast real-time review but their preference has no significant correlation with the feature. Among the types of game broadcast real-time review the type with translucent text bubble took the highest visual attention. The above shows that the users' visual behavior has the tendency of special style. The visual attention analysis results of different types and locations of game broadcast real-time review in the study can play a guiding role in the interface design of real-time review feature in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.335-343
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• 2014

It is more important to ensure the credibility of internet media which influence the public opinion. However, there are vague suspicions in public from the examples of manipulation of online reviews with anonymity. In this study, we explore the possibility of manipulating public opinion in online web sites. We investigate the characteristics of comments posted by users on web sites and compare each comments by using the cosine similarity function. Our result shows followings. First, we found a correlation between the similarities of comments and the article ranks in the web sites. Second, it is possible to identify abnormal user activities indicating excessive multiple posting, double posting and astroturf activities.

• 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.

• 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.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1303-1312
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• 2018

The study analyzed the evaluation of the advertisement effect by the score and the method of the advertisement comments in ad evaluation in online site, 'TVCF'. The results are as follows. First, Internet viral advertisement showed higher number of ad comments and higher evaluation of advertisement effect than cable broadcasting advertisement. Second, the results of analysis of the difference of advertisement evaluation according to ad types and digital user characteristics showed that women are more positive than men toward both cable broadcasting and internet viral advertisement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.355-360
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• 2021

Recently Youtube has been more popular. As many studies show the comparative deprivation of the Social Medeia, this study looks into whether the comparative deprivation is expressed on the YouTube comments. It focuses on the Luxury Haul contents, videos about huge amounts of luxurious products, of which Youtubers'economic feature are demonstrative. The comments of the videos are analyzed with LDA TF-IDF and Word2Vec. Additionally, the comments were classified into positive and negative groups by the LSTM model as well. As a result of the study, even though many comments turned out positive, the negative keywords were indicated related to comparative deprivation. Also it was found that the viewers compared themselves with Youtubers. In particular, some YouTubers are more criticized if they are younger or does not seem to afford the luxurious products themselves. This study suggests that the users express the comparative deprivation on YouTube as well like on the other Social Media.

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.55-63
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• 2018

Most people today search for information on the Internet about the goods or services they want to purchase and then assess the replies posted by other people who have experience with those goods or services. These replies serve as an important reference point that can affect purchase decisions. Replies are divided broadly into two types: first, market normative replies about whether a person experiences satisfaction with (or more than) the price paid for goods or services (positive) or not (negative); and the second is non-market normative replies about whether the goods or service provider morally deserves the profits gained from providing them (positive) or not (negative). Previous studies on replies have focused on market normative replies (whether the food is delicious), and there have only been some studies on the effect of non-market normative replies (the owner is morally good). This research was undertaken to re-examine the effect of market normative replies identified by previous studies in a restaurant visit intention evaluation (Experiment 1), to examine the effect of non-market normative replies not investigated in previous studies (Experiment 2), and to compare the effect of market normative replies and non-market normative replies (the meta-analysis) In conclusion, restaurant visit intention was stronger when market normative replies were positive (delicious) than when they were negative (not delicious) (Experiment 1). Furthermore, restaurant visit intention was stronger when non-market normative replies were positive (the owner is moral) than when they were negative (the owner is immoral) (Experiment 2). On the other hand, it was found that restaurant visit intention was stronger when non-market normative replies were positive than when market normative replies were positive, and restaurant visit intention was weaker when non-market normative replies were negative than when market normative replies were negative. This implies that people are more likely to be affected by non-market normative replies than market normative replies. In addition, this study suggested that the mood changed more before and after checking non-market normative replies than before and after checking market normative replies, and due to this difference, people could be affected more by non-market normative replies than market normative replies.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.735-736
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• 2021

인터넷에서 댓글 시스템은 자신의 의사표현을 위한 시스템으로 널리 사용되고 있다. 하지만 이를 악용하여 상대방에 대한 혐오를 드러내기도 한다. 악성댓글에 대한 적절한 대처를 위해 빠르고 정확한 탐지는 필수적이다. 본 연구에서는 악성 댓글 분류 문제를 해결하기 위해서 순서가 있는 분류 레이블의 성질을 활용한 순서형 회귀 (Ordinal regression) 기반의 분류 모델을 제안한다. 일반적인 분류 모형과는 달리 혐오 발언 정도에 따라 다중 레이블을 부여하여 학습을 진행하였다. 실험을 통해 Korean Hate Speech Dataset에 대해 LSTM기반의 모형의 출력층을 다르게 구성하여 순서형 회귀 기반의 모형들의 성능을 비교하였다. 결과적으로 예측 결과에 대한 조율이 가능한 순서형 회귀 모형이 일반적인 순서형 회귀 모형에 비해서 편향된 예측에 대해 추가적인 성능 향상을 보였다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.577-580
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• 2023

악성댓글은 인터넷상에서 정서적, 심리적 피해를 주는 문제로 인식되어 왔다. 본 연구는 한국어 악성댓글 탐지 분석을 위해 KcBERT 및 다양한 모델을 활용하여 성능을 비교하였다. 또한, 공개된 한국어 악성댓글 데이터가 부족한 것을 해소하기 위해 기계 번역을 이용하고, 다국어 언어 모델(Multilingual Model) mBERT를 활용하였다. 다양한 실험을 통해 KcBERT를 미세 조정한 모델의 정확도 및 F1-score가 타 모델에 비해 의미 있는 결과임을 확인할 수 있었다.