• The Journal of Society for e-Business Studies
• /
• v.15 no.3
• /
• pp.45-61
• /
• 2010

This study investigated the types of intention to use micro-blogging service. In this study, we classified micro-blogging users' motivations using Q methodology which enables measure objectivity with subjective activity like individual thinking and feeling. The results of this study showed that micro-blogging service users' motivationswere classified into four types. Type 1 is 'relationship oriented type' and Type 2 is 'self-expression type.' Type 3 is 'time consumption type' and Type 4 is 'information seeking type.' The findings imply that the characteristics of each user type can be utilized to customize micro-blogging services.

• Journal of Korea Game Society
• /
• v.12 no.3
• /
• pp.77-86
• /
• 2012

In recent years, micro-blogging services such as 'Twitter' and 'Me2day' have rapidly become major social networking services. However, it is difficult to grasp the relationship between a user and his/her friends in these micro-blogging services because they simply list messages between them in chronological order. In this paper, we propose a visual analytics system that can help the user intuitively understand relationships with their friends on micro-blogging services by enabling them to analyze the messages quantitatively, qualitatively and temporally. In the visual analytics system, we also present a tool to provide the user with valuable advices after classifying the changing relation patterns with his/her friends, which in turn contributes to improving relationships with friends. The proposed system was successfully implemented as smartphone applications to show its potential to be a tool for analyses and improvement of social relations in micro-blogging services.

• Journal of Digital Convergence
• /
• v.10 no.4
• /
• pp.341-346
• /
• 2012

Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.

• Journal of the HCI Society of Korea
• /
• v.5 no.1
• /
• pp.37-43
• /
• 2010

Twitter has been continuously growing since October, 2006. Especially, not only the users and the number of messages have been increasing but also a new concept in social networking called 'micro blogging' has diffused. Within Korea, service such as 'me2day' has already been introduced and the improvement of internet accessibility within mobile devices is expected to expand the 'micro blogs'. In this point, this research is executed to study the new medium, 'micro blog'. To do so, we collected and analyzed Twitter logs of Korean users. Especially, we were curious about the extreme heavy users using Twitter, despite of the linguistic and cultural barrier of the foreign service. Who they are, why and how they use the 'micro blog'. First, we reviewed the general aspect of followers and messages by collecting a certain number of random samples. Using the Lorenz curve we found out that there was the imbalance within the users and based on this phenomenon we deducted an extreme heavy user group. In order to perform further analysis, log analysis was performed on the extreme heavy users. As the result, the users used multiple mobile and desktop 'Twitter' clients. The usage pattern was similar to that of internet usage time but was used during their "micro" time. The users using 'Twitter' not only to spread messages about important information, special events and emotions, but also as a habitual 'chatting tool' to express ordinary personal chats similar to SMS and IM services. In this research, it is proved that 68% of the total messages were ordinary personal chats. Also, with 24% of the total messages were retweets, we were able to find out that virtually connected 'people' and 'relationships' acted as the dominant trigger of their articulation.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2011.05a
• /
• pp.27-28
• /
• 2011

• Journal of Information Science Theory and Practice
• /
• v.2 no.3
• /
• pp.29-39
• /
• 2014

In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

• Journal of The Korean Association of Information Education
• /
• v.15 no.4
• /
• pp.571-578
• /
• 2011

In this study, we suggested a strategy about a discussion class using Twitter, and experimented it inside an elementary school classroom. Elementary students participated in a panel discussion and the others discussed as audience using Twitter. After the discussion, we investigated the effectiveness of our strategy using the Technology Acceptance Model and verified students' satisfaction and ability to collaborate through giving them a questionnaire. As a result, the perceived ease of use positively effected the perceived usefulness and the perceived usefulness influenced the attitude and the attitude affect on intention to use. Also, students were satisfied with the discussion class on Twitter and had a positive perception about collaboration with it. As a result of regression, perception of collaboration among the students influenced the perceived usefulness positively. The results in this study show the effectiveness of using the discussion class strategy on Twitter.