• Journal of Digital Contents Society
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• v.12 no.2
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• pp.195-203
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• 2011

In-person lectures have structural issues that active communications in the classroom are limited because of the environments where the instructor usually delivers learning contents in a unilateral manner. Therefore, microcontents activities using real-time microblogging were suggested as complementary measures for the lecture in this study. Fourteen students in K University participated in the learning activity for eight weeks using a microblog during instructions. As a result, it was found that participants' positive learning activities increased by producing and collaborating ideas through real-time microblogging. Based on the results, suggestions were made as follows: strategies for the attention to the class, quality management of microcontents, and the development of blended learning design should be more studied further.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.295-300
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• 2011

Twitter is a popular microblogging service that enables the users to send and read short text messages. These messages are becoming source to analyze topic trends and identify relations among temporal topics. In this paper, we propose a method to classify the temporal topics on Twitter as a problem of grouping the similar patterns. To provide a starting point for a classification under the same topics, we identify the content word weighting scheme based on Latent Dirichlet Allocation (LDA). And we formulate how the temporal topics in the time window can be classified like peaky topics, constant topics, and periodic topics. We provide different real case studies which show the validity of the proposed method. Evaluations show that the proposed method is useful as a classifying model in the analysis of the temporal topics.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.7-13
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• 2014

Twitter is a microblogging service that allows users to post short messages on a variety of topics in real-time. In this work, we analyze Twitter messages posted during the 2012 elections and find those implications. This study uses Twitter messages related to the 2012 South Korean presidential campaign. The three main candidates are represented by the abbreviations A, M, and P. According to the statistical analysis, the number of tweets and re-tweets for candidate P was relatively stable over the entire campaign period. Candidate P had the highest percentage of terms related to elections pledges, and candidates A and M were judged to be a little bit poorer with respect to campaign promises. The positive terms ratio for candidate P was higher than those for the other two candidates. The negative terms ratio in the Twitter messages of P was considerably smaller than those of candidates A and M. After considering all these results, it is suggested cautiously that Twitter messages posted during an election campaign could be correlated with the outcome of the election.