• Korean Journal of Fisheries and Aquatic Sciences
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• v.51 no.5
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• pp.600-605
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• 2018

The Indo-Pacific bottlenose dolphins Tursiops aduncus are the only cetacean species that can be observed visually on coastal areas of Jeju Island and regarded as one of valuable tour resources. We attempted to figure out location and timing information of the dolphin sightings collected from two major social media, Naver $Caf{\acute{e}}$ and Instagram. 142 of dolphin sighting information were derived from 2,501 dolphin related postings on Naver $Caf{\acute{e}}$ between 2004 to 2017. 292 informative postings also were found on Instagram through hashtag searching. The number of posts about dolphin sighting was not frequent until 2014. Since 13 posts were found in 2014, dramatic increase of the sighting numbers was accelerated as 38 in 2015, 93 in 2016 and 269 in 2017. 195 (45.7%) from coastal area of Daejeong-eup, south-western part of the Island, were posted. The number of dolphin sightings also high from Gujwa-eup(n=50, 11.7%), Hangyeong-myeon (n=49, 11.5%), Seongsan-eup (n=38, 8.9%) and Seogwipo-si (n=34, 8.0%). Our results show that social media data has a high potential to be used as a data source for study of distribution pattern of the dolphins.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.211-219
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• 2013

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.199-209
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• 2019

The importance of business strategy for successful social media engagement has quickly increased as more businesses engage in social media. The importance is even greater for startup companies because startup companies are genuinely new to business, and they need to increase their presence in the market, and quickly access future customers. The objective of this paper lies in exploring key indicators of social media engagements by selected startup companies. The key indicators include two aspects of social media usages by the companies: i) overall social media activities, and ii) properties of network structure of the information flow platform provided by social media service. To better assess and evaluate the key indicators of social media usages by startup companies, the indicators will be compared with those of selected large established companies. Twitter is selected as a social media service for the analysis of this paper, and using Twitter REST API, data regarding the key indicators of overall Twitter activities and the Twitter follower-network of each company in the sample are collected. Then, the data are analyzed using social network analysis and hierarchical clustering analysis to examine the characteristics of the follower-network structures and to compare the characteristics between startup companies and established companies. The results show that most indicators are significantly different across startup companies and established companies. One key interesting finding is that the startup companies have proportionally more influencers in their follower-networks than the established companies have. Another interesting finding is that the follower-networks of startup companies in the sample have higher modularity and higher transitivity, suggesting that the startup companies tend to have a proportionally larger number of communities of users in their follower-networks, and the users in the networks are more tightly connected and cohesive internally. The key business implication for the future social media engagement efforts by startup companies in general is that startup companies may need to focus on getting more attention from influencers and promoting more cohesive communities in their follower-networks to appreciate the potential benefits of social media in the early stage of business of startup companies.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.7
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• pp.371-378
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• 2017

Recently, with the spread of smart device and the introduction of web services, the data is rapidly increasing online, and it is utilized in various fields. In particular, the emergence of social media in the big data field has led to a rapid increase in the amount of unstructured data. In order to extract meaningful information from such unstructured data, interest in big data technology has increased in various fields. Big data is becoming a key resource in many areas. Big data's prospects for the future are positive, but concerns about data breaches and privacy are constantly being addressed. On this subject of big data, where positive and negative views coexist, the research of analyzing people's opinions currently lack. In this study, we compared the changes in peoples perception on big data based on unstructured data collected from the social media using a text mining. As a results, yearly keywords for domestic big data, declining positive opinions, and increasing negative opinions were observed. Based on these results, we could predict the flow of domestic big data.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.295-305
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• 2016

The purpose of this study is to analyze the internal reputation factors that affect the job satisfaction by big data analysis in the social media for corporate reputation and verify the difference between large corporations and small-medium corporations for each factor of internal reputation. The result showed 'Salaries and Benefits' is a major factor that affects the job satisfaction for all research corporations, 'Senior Management' is a major factor for large corporations, and 'Salaries and Benefits' is a major factor for small-medium corporations. As for the difference factors of large corporations and small-medium corporations are 'Job Satisfaction', 'Salaries and Benefits', and 'Work-life Balance'. Unstructured data analysis shows some interesting features to be studied further.

• International Journal of Knowledge Content Development & Technology
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• v.4 no.2
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• pp.25-47
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• 2014

The goal of the present study is to explore IM interruption by relating it to media choices and purposes of IM use in the workplace. Two major media choice concepts were: media richness and social influence; while four purposes of IM use were: organization work, knowledge work, socializing, and boundary spanning activities. Data (N = 283) were collected via a combination of convenience and snowball sampling of "computer-using workers" in Taiwan, based on the Standard Occupational Classification system published by the Taiwan government. Results indicated that media choice works better than purpose of IM use to explain IM interruption. Among them, social influence was the best predictor to IM interruption in the workplace. In addition, instant feedback and personalization provided by IM, and IM usage for the purposes of knowledge work and socializing, also relate to IM interruption in the workplace.

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.153-176
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• 2016

This study investigated factors affecting the use intention of podcast service users based on the unified theory of acceptance and use of technology (UTAUT). Performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, innovativeness, and media credibility were used as independent variables in the model. The survey data from the users of the podcast portal 'podbbang' were analyzed with Smart PLS 2.0 to test the structural equation model. The results revealed that the podcast service user's effort expectancy, facilitating condition, hedonic motivation, and media credibility have a significant influence on use intention. However, the relationship between the podcast service user's performance expectancy, social influence, innovativeness, and use intention were not identified as significant.

• The Journal of Bigdata
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• v.4 no.1
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• pp.99-118
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• 2019

This study used network analysis and clustering techniques to analyze studies on social media participation. As a result of the main path analysis, 37 major studies were extracted and divided into two networks: community-related networks and new media-related. Network analysis and clustering result in four clusters. This study has the academic significance of using academic data to grasp research trends at a macro level and using network analysis and machine learning as a methodology.

• Journal of Information Technology Applications and Management
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• v.27 no.5
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• pp.69-80
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• 2020

Recently, luxury brands are selling their products to consumers using their own direct online channels. In the online channel, marketing strategy through storytelling is needed because consumers do not have enough product experience. Therefore, luxury brands are actively utilizing social media and delivering stories includes their birth and growth. Unlike mass media, social media communicates with consumers more quickly and frequently and delivers the story of brand naturally. This study classifies luxury brands into four groups based on story recognition of luxury brands and self-esteem, and analyzes and materializes each group of the propensities of luxury brand consumption. It also tries to draw strategic implications for effective SNS advertising by analyzing narrative transportation on SNS advertising, interests in videos, and the interests in story based on these typified groups of luxury consumption. The result of the analysis shows that there is a difference in consumption propensity among consumers who were classified into four groups according to story cognition of luxury brands and self-esteem. There is also a difference in the response to narrative images through SNSs, such as narrative transportation, interests in videos, and interests in brand stories.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.5
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• pp.620-633
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• 2019

The popularity of cosmetic procedures has increased over the past decade. Korean consumers have shown different levels of demand for the two types of cosmetic procedures (surgical and nonsurgical). This study examines the effect of appearance pressure from family, friends, and social media on the internalization and face/body satisfaction that can determine the levels of acceptance for each cosmetic procedure among young Korean females. Data was collected from 379 females in their 20s and 30s, using an online survey questionnaire. Statistical analysis were performed using SPSS 24.0 and AMOS. The results indicated that only social media pressure significantly influenced young women's internalization of attractive appearance. Internalization was negatively associated with face and body satisfaction. Face satisfaction was negatively related to the acceptance of cosmetic surgical and nonsurgical procedures; however, body satisfaction was not related to the acceptance of cosmetic surgical and nonsurgical procedures. This study highlights the significant importance of social media and its powerful impact on developing young women's body image perceptions.