• Asian Journal for Public Opinion Research
• /
• v.1 no.4
• /
• pp.249-267
• /
• 2014

Social media is being increasingly utilized in disaster relief work to identify safety issues, locate displaced-victims, and seek or provide support for those who need help. The presence of social media in disaster management has changed significantly in recent years, as it was not prevalent in the 2008 Wenchuan earthquake, but had become a powerful force in the 2013 Ya'an earthquake. This paper discusses the development of social media in disaster management via making a comparison between how it functioned in the two earthquakes. It examines the following aspects: who are the stakeholders that use social media in the earthquake management; how do they adopt this means in response to the earthquakes; and what are the outcomes of adopting social media with regards to public engagement and collaboration in an emergency event. As Sina Weibo acts as the equivalent of China's Twitter, the methodology relies on an analysis of posts in Weibo. The outcomes primarily show that: (1) authorities, celebrities and the public actively adopted social media for the purpose of information dissemination and resource mobilization; and (2) social media users are both content consumers and content generators in the times of earthquakes. The study concludes that social media as a backchannel communication tool is helpful for government institutions, corporations, and nonprofit organizations to build relationships with their stakeholders in the disaster management cycle. The result will interest academics and emergency management practitioners who are concerned with improving disaster communication.

• 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.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.85-94
• /
• 2013

With the emergence of world wide web, in particular web 2.0 the rapidly growing amount of news articles has created a problem for users in selection of news articles according to their requirements. To overcome this problem different clustering mechanism has been proposed to broadly categorize news articles. However these techniques are totally machine oriented techniques and lack users' participation in the process of decision making for membership of clustering. In order to overcome the issue of zero-participation in the process of clustering news articles in this paper we have proposed a framework for clustering news articles by combining users' judgments that they post on twitter with the news articles to cluster the objects. We have employed twitter hash-tags for this purpose. Furthermore we have computed the credibility of users' based on frequency of retweets for their tweets in order to enhance the accuracy of the clustering membership function. In order to test performance of proposed methodology, we performed experiments on tweets messages tweeted during general election 2013 in Pakistan. Our results proved over claim that using users' output better outcome can be achieved then ordinary clustering algorithms.