• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.109-114
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• 2019

This study explored how the tendency of writing 'comment' by Facebook users and the number of 'like' in posting message affected to product attitude, purchasing intention. One hundred thirty five male and female college students were divided into groups with high/low tendency of writing 'comment'. The subjects had to read posting message about athlete shoes on Facebook's newsfeed, different from the conditions under which the 'like' in the posting was high and low. Then, they were responded product attitude and the intention of purchasing. The results of two-way ANOVA showed that the users with low tendency of writing 'comment' displayed more positive product attitude and higher willingness to purchase under condition with a high 'like' number of posting than under condition with a low 'like' number of it.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.194-204
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• 2016

With the fast diffusion of smart media, Webtoon has become popular contents among Korean people. Webtoon's content is being used in various content industries, such as movies and drama, and thus its cultural influence is increasing. Using ordinal Regression analysis, this study tried to find major factors affecting webtoon's success. This study found that readers' rating, number of likes, OSMU, author power, genre, picture style are important factors affecting the success of webtoon. This study has several business implications for the Korean webtoon industry.

• The Journal of Bigdata
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• v.1 no.1
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• pp.61-66
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• 2016

With big data analysis, companies use the customized marketing strategy based on customer's information. However, because of the concerns about privacy issue and identity theft, people start erasing their personal information or changing the privacy settings on social network site. Facebook, the most used social networking site, has the feature called 'Likes' which can be used as a tool to predict user's demographic profiles, such as sex and age range. To make accurate analysis model for the study, 'Likes' data has been processed by using Gaussian RBF and nFactors for dimensionality reduction. With random Forest and 5-fold cross-validation, the result shows that sex has 75% and age has 97.85% accuracy rate. From this study, we expect to provide an useful guideline for companies and marketers who are suffering to collect customers' data.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.269-276
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• 2015

The today's rapid spread of smartphones makes it easier to use SNS. However, it reveals only their daily life or interest. Therefore, it is hard to really get to know the detailed part of multi-user's common interests. This paper proposes a content recommendation system which recommends people wanted by identifying common interests through SNS. Recommendation system includes proposal formula considering people wanted and deviation in group. After simulation, the proposed system provide high-quality adapted contents to many users by recommendation item according to the common interest. Number of cases about formula are four. It recommend contents that they have many number of 'like' and few number of deviation in users. The proposed system proves by simulations of four cases and read user's 'likes' data. It provide high-quality adapted contents to many users by recommendation item according to the common interest.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.115-125
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• 2022

This study examines the effect of performers on the number of views and likes of entertainment show clips consumed on AVOD short form platform. Multiple regression analysis was performed, setting program viewing factors and performer's topicality index as independent variables, and setting the number of views and likes of clips as dependent variables. As a result of the analysis, performer's topicality index had a positive(+) effect on both dependent variables. According to standardized coefficient, on the number of views, the standardization coefficient of the performer's topicality index was the second highest, and on the number of likes it was the highest among variables. The results suggest that performers contribute a lot to the success of clips on AVOD short form platform.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.1-19
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• 2023

Despite the growing importance of video-based social media content, such as vlogs, as a marketing tool in the travel industry, there is limited research on the characteristics that enhance engagement among potential travelers. This study explores the influence of emotional valence in YouTube travel content on viewer engagement, specifically likes and comments. We analyzed 4,619 travel-related YouTube videos from eight popular tourist cities. Using negative binomial regression analysis, we found that both positive and negative emotions significantly influence the number of likes received. Videos with higher positive emotions as well as negative emotions receive more likes. However, when it comes to the number of comments, only negative emotions showed a significant positive influence, while positive emotions had no significant impact. These findings offer valuable insights for marketers seeking to optimize engagement strategies on YouTube, considering the unique nature of travel products. Further research into the effects of specific emotions on engagement is warranted to improve marketing strategies. This study highlights the powerful impact of emotions on viewer engagement in the context of social media, particularly on YouTube.

• Annual Conference of KIPS
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• 2019.10a
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• pp.770-773
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• 2019

본 논문에서는 매년 성장하는 웹툰 시장 속에서 신인 작가들이 성공할 수 있는 성공 요인을 밝히고자 하였다. 국내 1위 웹툰 플랫폼인 네이버 웹툰 중 데뷔작을 기준으로 완결 웹툰 212개, 연재 중인 웹툰 112개, 총 324개의 웹툰을 수집하여 연구를 진행하였다. 기존 선행연구와의 차별화를 두기 위해 독자의 직접적인 반응 중 하나인 댓글을 성공 요인에 포함하였다. 댓글에 담긴 긍정, 부정을 나타내는 주관을 탐지하기 위해 딥러닝을 이용하여 감성 분석을 실시하였다. 각 웹툰에 대한 댓글 반응을 포함하여 평균, '좋아요' 수, 장르 그리고 첫 화 댓글 수와 5화까지 평균 댓글 수를 흥행에 영향을 미치는 독립변수로 사용했다. 댓글 반응이 중요 요인인지를 확인하기 위해 각 모델 생성 시 댓글 반응을 포함한 모델과 포함하지 않은 모델을 생성하여 성능 평가를 실시하였다. 로지스틱 회귀분석, 아다 부스트, 그리고 서포트 벡터 머신 모델을 정확도와 ROC 그래프를 이용해 효율성을 비교하고, 이를 통해 댓글 반응을 활용한 로지스틱 회귀 모델이 가장 적합하다고 판단하였다. 모델 생성 결과 '좋아요' 수, 1화 댓글 수, 댓글 반응 순으로 성공 요인에 많은 영향을 미치는 것을 알 수 있었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.663-669
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• 2016

This study explores the effect of tagging in Social Network Services, especially Facebook, which has become popular as a marketing platform. In Facebook, users generally make recommendations using 'Like', 'Share', or 'Tag'. 'Tag' is different from 'Like' or 'Share' in that it can be used to deliver certain messages directly to specific people based on their interests or characteristics. Tagging can be categorized into rewarded tagging and non-rewarded tagging. As a result of our exploratory research, we found that non-rewarded tagging by certain users can indicate that the people, who are tagged, are interested in the contents of the users and share the same interest as them. Also, tagging indicates that these users want to share these services, such as restaurants and tours, with their friends who are tagged in the contents. Therefore, this study sheds light on the importance of the tagging function, as well as 'Like' and 'Share'.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.410-422
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• 2017

This study researches PDA (Paralingustic Digital Affordances) in the social media on the basis of Uses and Gratification theory. The study defines PDA as Likes in Facebook and Instagram and Favorites in Twitter. The study inquiries into the motivation of using PDA and interpretational way when Social media users play a role of a sender or a receiver. For this research purpose, the focus group and interview were conducted with 36 college student in the Korea metropolitan area. The research is to comprehend the motivation and satisfaction of using PDA by applying structured theory frame of Uses and Gratification. As a result, it contributes to more satisfactions when PDA users interact each other as a sender and a receiver than mere verbal-communication. Furthermore, PDA in each social media has different meaning and gravity. For instance, Likes in Instagram is considered less important and lighter than Likes in Facebook. Moreover, people use the PDA without any restriction. People favorably use PDA most of the time, but sometimes they use in contradictory or sarcastic way.