• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.460-476
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• 2015

The purpose of this study is to provide practical implications on government crisis management strategies and on the use of SNS in crisis management. Specifically, this study analyzed Ministry of Drug and Food Safety's responses to paraben toothpaste issue, media coverage of paraben toothpaste issue, and public responses to paraben toothpaste issue. Through textual analysis and the network analysis of 45 news articles and 645 tweets, this study found that Ministry used one-way communication strategy and mostly negative issues regarding Ministry's crisis response strategies were diffused via the media and Twitter. This study was meaningful in that it highlighted the importance of media relations and use of SNS in crisis management. The findings of this study provide useful implications for government officials and PR practitioners in their crisis management and communication strategy.

• International Journal of Knowledge Content Development & Technology
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• v.5 no.1
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• pp.15-32
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• 2015

This article analyzes motivations behind social sciences blog posts citing journal articles in order to find out whether blog citations are good indicators for the societal impact or benefits of research. A random sample of 300 social sciences blog posts (out of 1,233 blog posts) from ResearchBlogging.org published between 01/01/2012 to 18/06/2014 were subjected to content analysis. The 300 blog posts had 472 references including 424 journal articles from 269 different journals. Sixty-one (22.68%) of all cited journals were from the social sciences and most of the journals with high frequency were highly cited general science journals such as PNAS and Science. Seventy-five percent of all journals were referenced only once. The average age of articles cited at the time of citation was 5.8 years. Discussion and criticism were the two main categories of motivations. Overall, the study shows the potential of blog citations as an altmetric measure and as a proxy for assessing the research impact. A considerable number of citation motivations in blogs such as disputing a belief, suggesting policies, providing a solution to a problem, reacting to media, criticism and the like seemed to support gaining societal benefits. Societal benefits are considered as helping stimulate new approaches to social issues, or informing public debate and policymaking. Lower self-citation (compared to some other altmetric measures such as tweets) and the fact that blogging involves generating content (i.e. an intellectual process) give them an advantage for altmetrics. However, limitations and contextual issues such as disciplinary differences and low uptake of altmetrics, in general, in scholarly communication should not be ignored when using blogs as a data source for altmetrics.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.71-75
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• 2017

Due to the spread of SNS, web-based consumer-generated data is increasing exponentially. It is important in many fields to accurately extract what is appropriate for the user's interest in a large amount of data. It is especially important for business mangers to establish marketing policies to find the right customers for them in many users. In this paper, we try to obtain important information centering on customers who are interested in each account through Twitter follow - following relationship. Because Twitter's current follower relationships do not reflect the user's interests, we try to figure out the details of interest using keyword extraction methods for tweets of followers. To do this, we select two domestic commercial Twitter accounts and apply the sequential pattern evaluation index to the mining key phrase of the text data collected from the follower.

• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1380-1386
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• 2015

Recently, the importance of users' geographic location information has been highlighted with a rapid increase of online social network services. In this paper, by utilizing geo-tagged tweets that provides high-precision location information of users, we first identify both Twitter users' exact location and the corresponding timestamp when the tweet was sent. Then, we analyze a relationship between the tweet frequency and the average user velocity. Specifically, we introduce a tweet-frequency computing algorithm, and show analysis results by country and by city. As a main result, it is shown that the tweet frequency according to user velocity follows a power-law distribution (i.e., Zipf' distribution or a Pareto distribution). In addition, by performing a comparison between the United States and Japan, one can see that the exponent of the distribution in Japan is smaller than that in the United States.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2885-2891
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• 2015

Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users' exact location and the corresponding timestamp, and then propose an improved two-stage tweet bot detection algorithm by computing an entropy based on spatio-temporal information. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.263-285
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• 2016

Nowadays, Social Network Service (SNS), which has been in the spotlight as a way of communication, has become a most effective tool to improve easy of information use and accessibility for users. In this paper, we chose Twitter as the most representative SNS services because of automatic crawling and investigated tweet data gathered from domestic and foreign National Archives - NARA of U.S.A., TNA of U.K.. NAA of Australia, and National Archives of Korea. We also conducted information genres analysis and trend analysis by timeline. Information genres analysis shows how archives satisfied users' information needs as well as trends analysis of tweets helps to understand how users' interestedness was changed. Based on comparison results, we distilled four characteristics of National Archives and suggested vitalization ways for National Archives of Korea.

• Journal of KIISE
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• v.43 no.11
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• pp.1201-1209
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• 2016

As the use of SNS continues to increase, various related studies have been conducted. According to the effectiveness of the topic model for existing theme extraction, a huge number of related research studies on topic model based analysis have been introduced. In this research, we suggested an automation system to analyze topics of each country and its distribution in twitter by combining world map visualization and issue matching method. The core system components are the following three modules; 1) collection of tweets and classification by nation, 2) extraction of topics and distribution by country based on topic model algorithm, and 3) visualization of topics and distribution based on Google geochart. In experiments with USA and UK, we could find issues of the two nations and how they changed. Based on these results, we could analyze the differences of each nation's position on ISIS problem.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.137-147
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• 2013

The purpose of this study is to examine what variables influence on tweet credibility and attitude toward tweet. For this, the present research used the tweet content(information/opinion), tweet function(without URL and RT/URL/RT) and involvement(low/hight) as independent variables and applied a triangular research design which are in-depth interview, survey and computer usability testing software. Main findings are as follows. First, the participants read tweets listed in order regardless of tweet content, function and involvement. Second, there was a significant main effect of the tweet content on the tweet credibility and an interaction effect of those three independent variables on the attitude toward tweet. Finally, the in-depth interview showed that information is perceived to be more credible than opinion and URL>RT>just information or opinion are listed in order on the tweet credibility.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.24-36
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• 2015

Recently, interests in detecting hot topics have been significantly growing as it becomes important to find out and analyze meaningful information from the large amount of data which flows in from social network services. Since it deals with a number of random writings that are not confirmed in advance due to the characteristics of SNS, there is a problem that the reliability of the results declines when hot topics are predicted from the writings. To solve such a problem, this paper proposes a high reliable hot topic prediction scheme considering user influences in social networks. The proposed scheme extracts a set of keywords with hot issues instantly through the modified TF-IDF algorithm based on Twitter. It improves the reliability of the results of hot topic prediction by giving weights of user influences to the tweets. To show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation. Our experimental results show that our proposed method has improved precision and recall compared to the existing method.