• Journal of Information Technology Applications and Management
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• v.14 no.3
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• pp.115-136
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• 2007

In this study, we investigate the possibility of deploying a recently emerging Internet-based technology, called Web log or Blog, to address the problems of knowledge transfer and sharing, particularly in the case of tacit knowledge. We examined the use practice of four blogs and then identified several properties relevant to knowledge transfer and sharing. They include the specific style of blog format, content ownership attribution, posted article organization, communication tools and method, news feed function, and various links from/to outside websites. These features were argued to facilitate knowledge transfer and sharing. In particular, we discussed a great deal about the structure of comments and links as tools for collaboration and idea sharing, which enables the knowledge conversion processes (socialization, externalization, combination, and internalization), We then provide several guidelines to develop blogs as a knowledge management tool.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1121-1125
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• 2010

This paper proposes a Wikipedia-based feedback method for in-depth blog distillation whose goal is to find blogs that represent in-depth thoughts or analysis on a given query. The proposed method uses Wikipedia articles which are relevant to the query. TREC Blogs08 collection which is a large-scale blog corpus and English Wikipedia dump were used for experiments, The proposed method significantly increased the retrieval performance including MAP over the conventional post based feedback method.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.248-249
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• 2004

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.05a
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• pp.208-209
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• 2005

• The Research Journal of the Costume Culture
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• v.24 no.5
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• pp.600-616
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• 2016

The purpose of this study was to investigate the effects of power blog self-presentation and on-line shopping mall reviews on the word of mouth (WOM) effect and apparel purchasing behavior. Consumers living in Seoul and Gyeonggi received questionnaires. There were 303 usable forms that could be analyzed by descriptive statistics, factor analysis, Cronbach's alpha, and regression analysis. The results were as follows: There was a significant effect of power blog self-presentation such as interactivity, media effect, and business intention on information acceptance through WOM. Power blog self-presentation, such as interactivity and business intention, affect information delivery by WOM significantly. There was a significant effect of review consideration, such as agreement, usefulness, enjoyment, and purity, on information acceptance of WOM. Reviews describing enjoyment, purity, and usefulness affect information delivery of WOM significantly. Business intention, media effect, and purity directly affect apparel-purchasing behavior, and affect the WOM effect as a mediator variable and then purchasing behavior. Whereas, interactivity, overstatement, and enjoyment affect the WOM effect as a mediator variable, and then affect overall purchasing behavior. Therefore, fashion firms consider active interaction with power blog visitors and promote the way of enjoying with fun through review of apparel on-line shopping mall when they try to implement viral marketing with WOM effect.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.56-69
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• 2010

In blogspace, there are posts that deal with a common topic and bloggers that are interested in these posts. In this paper, we define a blog community as a group of these bloggers and posts. With a blog community, we can establish various business policies for target marketing, sharing high quality data, and mobilizing the activities in the blogspace. Unlike internet cafes, bloggers participate in blog communities without explicit membership. So, it is not easy to identify the members of a community. In this paper, we propose an effective approach for extracting a blog community that is related to a given topic. First, we choose seed posts that is highly related to a given topic, and select bloggers that are related to the topic with the seed posts. Then, we select posts that are related to the topic with the selected bloggers. By repeating this, we find all the posts and bloggers that are members of the community related to a given topic in blogspace. We verify the superiority of the proposed approach by analyzing extracted blog communities.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.73-82
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• 2013

The purpose of this study is to explore attributes of trusted blog product-reviews and to examine the weight of each attribute. First, the attributes of trusted blog product-reviews were collected through consumer interviews. Second, the trust attributes were examined in terms of their relative importance. The results are: 1) Thirty-five of trust attributes were discovered and categorized into 'popularity', 'presence', 'attractiveness', 'trustworthiness', and 'expertise'. 2) In general, attributes reflecting usefulness, trustworthiness and attractiveness seemed the most important trust factors. 3) 'presence', which have not been highlighted so far in trust research, was emerged as an important trust factor in the web blog context. Theoretical and practical implications were discussed.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.17-27
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• 2020

News articles and blog posts have influence on social agenda setting and this study applied text mining on the subject of solar PV in rural area appeared in those media. Texts are gained from online news articles and blog posts with rural solar PV as a keyword by web scrapping, and these are analysed by sentiment analysis and topic modeling technique. Sentiment analysis shows that the proportion of negative texts are significantly lower in blog posts compared to news articles. Result of topic modeling shows that topics related to government policy have the largest loading in positive articles whereas various topics are relatively evenly distributed in negative articles. For blog posts, topics related to rural area installation and environmental damage are have the largest loading in positive and negative texts, respectively. This research reveals issues related to rural solar PV by combining sentiment analysis and topic modeling that were separately applied in previous studies.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.219-233
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• 2008

This paper suggests an intelligent semantic blog model to systematically analyze and manage biogosphere with ontology as its conceptual knowledge base. In the model, the system managers may support users to easily find appropriate blog resources by tracking and analyzing various relationships between ontology - they may intelligently recommend Interest blog communities to relevant users by monitoring interaction activities in blogoshpere, dynamically grouping the communities with the ontology. To systematically specify the functionality of our model, 1) we first express the structure of blog resources in terms of objects and relationships between them and then 2) we formalize a set of operators designed to be applied to the resources. System queries are implemented by the combination of the operators.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.