• Journal of Multimedia Information System
• /
• v.2 no.3
• /
• pp.287-294
• /
• 2015

We are living in epoch of information when Internet touches all aspects of our lives. Therefore, it provides a plenty of services each of which benefits people in different ways. Electronic Mail (E-mail), File Transfer Protocol (FTP), Voice/Video Communication, Search Engines are bright examples of Internet services. Between them Social Network Services (SNS) continuously gain its popularity over the past years. Most popular SNSs like Facebook, Weibo and Twitter generate millions of data every minute. Twitter is one of SNS which allows its users post short instant messages. They, 100 million, posted 340 million tweets per day (2012)[1]. Often big amount of data contains lots of noisy data which can be defined as uninteresting and unclassifiable data. However, researchers can take advantage of such huge information in order to analyze and extract meaningful and interesting features. The way to collect SNS data as well as tweets is handled by crawlers. Twitter crawler has recently emerged as a great tool to crawl Twitter data as well as tweets. In this project, we develop Twitter Crawler system which enables us to extract Twitter data. We implemented our system in Java language along with MySQL. We use Twitter4J which is a java library for communicating with Twitter API. The application, first, connects to Twitter API, then retrieves tweets, and stores them into database. We also develop crawling strategies to efficiently extract tweets in terms of time and amount.

• Journal of Digital Convergence
• /
• v.12 no.12
• /
• pp.249-255
• /
• 2014

Recently, creation and sharing of information make progress actively through the SNS(Social Network Service) such as twitter, facebook and so on. It is necessary to extract the knowledge from aggregated information and data mining is one of the knowledge based approach. Especially, emotion analysis is a recent subdiscipline of text classification, which is concerned with massive collective intelligence from an opinion, policy, propensity and sentiment. In this paper, We propose the emotion prediction method, which extracts the significant key words and related key words from SNS paragraph, then predicts the emotion using these extracted emotion features.

• The Journal of the Korea Contents Association
• /
• v.12 no.7
• /
• pp.336-345
• /
• 2012

Under the influence of information technology, there has been a significant change in the consumers' attitude and behavior toward the cultural products. Especially, the social network service (SNS) is predicted to effectively facilitate the growing interaction among potential consumers, which may lead to consumption of the cultural products. The goal of this study is thus two-fold: (a) to investigate the characteristic features of the digital environments based on SNS, and (b) to examine how these factors result in the purchase of the cultural contents. The survey data identified the digital environments as Informational interaction, Information connectivity, and Informational trust in the SNS environment. Subsequently, the structural equation methods confirmed that these factors facilitate consumers' participation in the information network and promote consumers' emotional attachment to the cultural contents, which eventually lead to the positive attitude toward the purchase of the cultural contents.

• Journal of The Korean Association For Science Education
• /
• v.36 no.5
• /
• pp.757-768
• /
• 2016

This study developed an SSI (Socio-Scientific Issue) discussion program that applies a creative technique called six thinking hats, and then investigated the differences in argumentation patterns and effects on the decision-making abilities of each character feature of students between SNS debate and existing face to face debate. There were three SSI themes - Designer Babies, embryonic stem cell study, and legitimacy of abortion. Students were divided into two groups, the debate group using SNS and face to face debate group. The character patterns of students were divided to 'extraversion,' 'agreeableness,' and 'conscientiousness' through test sheets for character features for each student. Both groups were educated for creative discussion methods using six thinking hats and then, the class progressed. As a result of analyzing argumentation patterns used in SNS debate and face to face debate, the most used argumentation pattern was the "cause pattern." But comparing to face to face debate, other patterns (mark, inference, authority, motive) were also used in SNS debate. The study analyzed three factors of decision-making ability for each character feature of students such as complexity, perspectives, and inquiry. As a result, for 'complexity' factor, there was a significant difference between SNS debate group and face to face debate group only in the student group of Agreeableness. For 'perspectives' factor, there were significant differences between SNS debate group and face to face debate group in all three characters. Finally, for inquiry, there were no significant differences between SNS debate group and face to face debate group in all three characters. Accordingly it would be necessary to apply SNS debate using the six thinking hats in SSI education to enhance perspectives.

• The Research Journal of the Costume Culture
• /
• v.26 no.4
• /
• pp.547-562
• /
• 2018

As social media penetrates more deeply into people's everyday lives, social commerce (a type of commerce that combines SNS features and possibility for commercial transactions) has enjoyed unprecedented growth. Shopping on Facebook is a representative example of social commerce platform that allows consumers to interact with other users, exchange information and purchase products without leaving a Facebook page. Social commerce presents great opportunities for marketers in terms of leveraging social aspects of shopping experience. It also offers a large potential for Korean companies to reach various target markets, as well as establish their presence abroad. Yet, acceptance of social commerce as a legitimate shopping channel has been slow, and consumers are still hesitant to shop via Facebook. This study draws on uses and gratification theory and the concept of perceived risk to examine how different motives for SNS use and the associated types of perceived risks can affect the purchase intention on the platform. Empirical data from 288 young users of Facebook were analyzed. Findings identified two main motives for SNS use: information-related motive and communication-related motive. Information-related motive significantly affected the intention to shop on Facebook, whereas communication-related motive did not have any significant influence. Risks associated with shopping via Facebook included delivery risk, security risk, social risk and economic risk. Overall, consumers perceived a higher level of security and social risk associated with shopping on Facebook. However, only social risk had a significant negative influence on the purchase intention. Awareness and previous experience of buying via social commerce platform positively affected consumers' purchase intention.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1643-1651
• /
• 2014

Social networking services (in short, SNS) allow users to share their own data with family, friends, and communities. Since there are many kinds of information that has been uploaded and shared through the SNS, the amount of information on the SNS keeps increasing exponentially. Particularly, Facebook has adopted some interesting features related to entertainment (e.g., movie, music and TV show). However, they do not consider contextual information of users for recommendation (e.g., time, location, and social contexts). Therefore, in this paper, we propose a novel approach for movie recommendation based on the integration of a variety contextual information (i.e., when the users watched the movies, where the users watched the movies, and who watched the movie with them). Thus, we developed a Facebook application (called MyMovieHistory) for recording the movie history of users and recommending relevant movies.

• Journal of Digital Convergence
• /
• v.10 no.1
• /
• pp.423-428
• /
• 2012

By shifting web2.0 users who share information from passive consumption and create their own information and exchange in the form of an active and visible appearance was changing. Most simply and easily with features that can be accessed. SNS is native to Korea me2day, Cyworld, (c) Logs and foreign SNS of Facebook, Twitter and a surge in user FramVille, Mafia War's Game, and many users use to SNG are. SNG's compared to the foreign national is active and not yet is a step. The domestic market, the benefits of this game online games and SNS in vogue these days to incorporate the concept in the market for a new form of the domestic game that the game, SNOG (Social Network Online Game, social networks, online games) to the expansion of flexible development direction, Expand accessibility, expansion of social skills is to present to the three.

• Journal of Information Processing Systems
• /
• v.17 no.5
• /
• pp.1020-1033
• /
• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

• International Commerce and Information Review
• /
• v.19 no.1
• /
• pp.99-126
• /
• 2017

The Indonesian e-commerce market has grown significantly due to sustained economic growth, middle class growth, rapid increase in Internet and SNS users, and increase in accessibility of mobile broadband services. In particular, consumers' online shopping through mobile and SNS has been increasing rapidly based on the expansion of the popularity of smart phone devices. This research suggested the strategies for expanding exports of Indonesia through e-commerce platform to the Korean firms, with deep analysis of the current status and features, problems, cases, and implications etc. of Indonesia's e-commerce market. As an export expansion strategy utilizing Indonesia's e-commerce platform, this study showed the Korean firms have to build a local online distribution network, establish a logistics & delivery and payment system, acquire Halal certification for Muslim market, carry out the in-depth market research, actively implement Hanryu marketing strategy, develop a creative product, set up market segmentation strategies, and develop SNS mobile marketing.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.5
• /
• pp.189-196
• /
• 2014

Recently, the need for retrieving documents is growing in SNS environment such as twitter. For supporting the twitter search, a clustering technique classifying the massively retrieved documents in terms of topics is required. However, due to the nature of twitter, there is a limit in applying previous simple techniques to clustering the twitter documents. To overcome such problem, we propose in this paper a new clustering technique suitable to twitter environment. In proposed method, we augment new terms to feature vectors representing the twitter documents, and recalculate the weights of features using Korean Wikipedia. In addition, we performed the experiments with Korean twitter documents, and proved the usability of proposed method through performance comparison with the previous techniques.