• Proceedings of the Korea Contents Association Conference
• /
• 2019.05a
• /
• pp.179-180
• /
• 2019

Nowadays, clustering algorithm is considered as a promising solution for lacking human-labeled and massive data of social media sites in numerous machine learning tasks. Many researchers propose disaster event detection systems have ability to determine special local events, such as missing people, public transport damage by clustering similar tweets and hashtags together. In this paper, we try to extend tweet hashtag feature definition by applying word embedding. The experimental results are described that word embedding achieve better performance than the reference method.

• Journal of Fashion Business
• /
• v.22 no.4
• /
• pp.20-29
• /
• 2018

Today, visitors of art galleries like to share their life in their communities than interacting with artwork. Meantime, image sharing of an exhibition on social media has become more important than actual watching of the artwork. Accordingly, most of the galleries have started paying more attention in organizing an exhibition environment for proof-shots to attract more visitors. We initially conducted research about the internet environment from the late 1990s to the recent years and analyzed the changing watching patterns of the exhibition since the advent of social media. Secondly, for empirical case analysis, we selected 'Plastic Fantastic' held in D-Museum as the target of analysis. The analysis targeted 500 recent postings that were discovered on Instagram on March 4, 2018, as 'Plastic-Fantastic'(in Korean). The methods of analysis included classification types of image, hashtag, and text on Instagram and were arranged in an order of relation to the exhibits. Based on the image analysis, 44.2% of the images involved exhibition displays; the others included a person or other goods. Based on the results of the text and hashtag analysis, only 3.6% of posting included information about the exhibition and 56.4% had non-related inflow hashtags only with image. The behavior of these shares is likely to gradually lose the inherent meaning of the exhibition and to the value rather than imparting the artistic thrill that viewers derive from art. Exhibition should try to seek deep interaction between the display, audience, and social media users, rather than encouraging the visitors to take proof-shots.

• Journal of Digital Convergence
• /
• v.11 no.6
• /
• pp.129-135
• /
• 2013

With the increasing use of the SNS, WOM(Word of Mouth) has become an even more powerful and useful resource for consumers and marketers. In this paper, we investigated the effects of message polarity (positive, neutral, negative) and type (factual, evaluative) on WOM through SNS (twitter). A total of 13.4 million twitter messages were collected and 1.0 million retweeted messages were analyzed. The results showed that message orientation, type, URL and hashtag have a significant (<0.01) effect on retweet counts and the interaction between message orientation and other factors were observed. It also observed that message type, URL and hashtag have significant (0.05) relationships with retweet speed.

• Journal of the FoodService Safety
• /
• v.4 no.1
• /
• pp.35-41
• /
• 2023

This study was based on the results of the study of 'Cheonyeon and Chunghyun Taste Alley Pilgrimage- Introducing Hidden Restaurants in Our Town', which was adopted as a project to revitalize urban regeneration as part of the Cheonyeon and Chunghyun Urban Regeneration New Deal project. This study was conducted in total of two stages, as a first step, the commercial district of Seodaemun Station was analyzed by analyzing the hashtag (#) mentioned along with the "Seodamun Station Restaurant" on Instagram from 2015 to 2020. As a result of the analysis, it was found to be an office commercial district related to "office workers", and it was found to be a commercial district with the characteristics of "small but certain happiness" where you can find hidden restaurants in front of your house. Based on the characteristics of these commercial districts, five stores utilizing the characteristics of the region were selected and cooking classes were conducted for students of Kyonggi University, who are local residents. The purpose of this study was to revitalize the aging Seoul city and contribute to the formation of positive relationships between local residents and merchants through cooking classes. In addition, the process was produced as digital media content and used as local promotional materials.

• International Journal of Contents
• /
• v.10 no.1
• /
• pp.36-42
• /
• 2014

Previous studies of the Korean wave have focused mainly on fan clubs by taking an ethnographic approach in the context of countries in Southeast Asia and, in a minor extension, Europe. This study fills the gap in the literature by providing a social network analysis of Tweets in the context of Mexico. We used the Twitter API in order to collect Twitter comments with the hashtag #kpop from March to August 2012, analyzing them with a set of webometric methodologies. The results indicate that #kpop power Twitterians in Mexico were more likely to be related to the public television broadcast. The sent Tweets were usually related to their programs and promotion for Kpop artists. These Tweets tended to be positive, and according to URLs, not only Kpop but also Korean dramas had considerable influence on the Korean wave in Mexico.

• Journal of Fashion Business
• /
• v.19 no.6
• /
• pp.112-126
• /
• 2015

In social environments, hashtags have been widely adopted and have become a new form of language for users. The current study attempts to enhance our understanding of users and their motivations to use hashtags when posting fashion-related information. Specifically, this study examines whether user characteristics (fashion leadership, conspicuousness) influence their motivations to use hashtags (curation, self-presentation, information diffusion), which then leads to behavioral intentions to continue to use hashtags and recommend the same to others. An online survey was administered to test our research questions. A total of 136 consumers in their 20s, 30s, and 40s living in Korea were used for data analysis. Structural equation modeling was conducted, which revealed that fashion leadership and conspicuousness had a positive impact on users' motivations of curation, self-presentation, and information diffusion. Motivations of self-presentation and information diffusions were found to affect users' behavioral intentions while curation had no significant impact. Practical implications are presented.

• Journal of Korea Multimedia Society
• /
• v.18 no.11
• /
• pp.1391-1399
• /
• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.07a
• /
• pp.369-372
• /
• 2018

박물관 3.0시대의 도래와 함께 박물관 경영 측면에서 빅데이터 분석, 그리고 공유와 개방의 관점 및 커뮤니케이션 플랫폼과 마케팅 도구로써 소셜 미디어의 영향력이 증대되고 있다. 모바일 애플리케이션이나 비콘에 의존했던기존의 박물관 빅데이터 분석과는 달리, 본 연구에서는 전시에 대한 인스타그램의 해시태그를 분석함으로써, 관람객 분석도구로써 인스타그램 해시태그의 효용성과 가치를 입증하는데 목적을 두고 있다. 이를 위해 최근 2년 동안 국내에서 개최된 다섯 개의 전시의 인스타그램 해시태그를 수집 및 시각화했다. 그 결과, 모든 전시의 인스타그램의 해시태그는 전시명, 전시장소, 전시회, 지역명, 작가명에 집중되었다. 결론적으로 인스타그램의 해시태그는 전시관람 경험에 대한 분석을 위한 빅데이터로 사용하는 것이 부적합했다. 또한 관람객 개발을 위한 도구로써 인스타그램 해시태그의 효용성과 가치는 입증되지 못한 반면, 노출형에 해당하는 해시태그의 정보 확산에 대한 잠재력은 확인되었다.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.501-511
• /
• 2020

As more consumers are interested in sustainability issues and evaluate brands based on their social responsibility issues involved, firms are using social media platforms in order to communicate with consumers about Corporate Social Responsibility (CSR) activities. However, the current literature focuses mostly on factors drive engagement of promotional messages, while little evidence was found in the literature on how to design effective CSR messages on social media to engage consumers. Using over 3,000 branded posts on Facebook and Twitter, this research explores factors that help CSR messages become more engaging in social media. The results show that, on average, CSR messages had a negative significant effect on consumer engagement. However, CSR messages became more engaging when designed with emotional appeals, longer texts, and a hashtag. While marginal, CSR messages with informational appeals and humor undermined the effect of CSR messages on engagement. Finally, we explore different types of CSR messages by their beneficiary scope and the role of brands in the message and discuss what message contents drive more engagement in social media. This research contributes to the academic literature and managers by providing new insights on how to design CSR messages for effective communications in social media.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.737-740
• /
• 2014

최근 SNS 사용자가 급증하면서 매우 다양하고 방대한 양의 글이 여러 종류의 SNS를 통해 생성되고 있다. 그중 트위터는 정보의 전달 및 확산에 상당히 유용한 도구로 사용되고 있다. 이러한 트위터의 사용자 트윗은 뉴스, 음악, 사진, 여행 등 다양한 형태로 등장한다. 또한 트위터는 해시태그라는 사용자 정의 태그를 사용하는데 이는 트윗의 키워드 및 핵심을 쉽게 표현할 수 있도록 해주는 효과적인 수단이다. 최근 상당히 많은 양의 트윗의 생성에도 불구하고 이를 다양한 카테고리별로 분류할 수 있는 연구가 많이 진행되지 않았다. 따라서 본 논문에서는 해시태그를 이용해 트윗의 핵심을 파악하고 수많은 트윗을 다양한 토픽별로 분류할 수 있는 기법을 제안한다. 우선 다양한 카테고리의 인기 해시태그가 포함된 트윗을 수집하고 수집한 트윗에서 해시태그별 키워드를 추출한다. 그리고 코사인 유사도를 통해 해시태그별 내용 유사도를 파악하여 각 카테고리 내의 해시태그가 얼마나 유사한 내용을 지니고 있는지 파악한다. 마지막으로 사용자 트윗이 입력되면 모든 카테고리와 유사도를 비교하여 가장 유사도가 높은 카테고리를 찾아 추천해준다. 제안된 기법을 바탕으로 프로토타입을 구현하고 실험을 통해 성능을 평가한다.