• International Journal of Contents
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• v.10 no.1
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• pp.36-42
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• 2014

Previous studies of the Korean wave have focused mainly on fan clubs by taking an ethnographic approach in the context of countries in Southeast Asia and, in a minor extension, Europe. This study fills the gap in the literature by providing a social network analysis of Tweets in the context of Mexico. We used the Twitter API in order to collect Twitter comments with the hashtag #kpop from March to August 2012, analyzing them with a set of webometric methodologies. The results indicate that #kpop power Twitterians in Mexico were more likely to be related to the public television broadcast. The sent Tweets were usually related to their programs and promotion for Kpop artists. These Tweets tended to be positive, and according to URLs, not only Kpop but also Korean dramas had considerable influence on the Korean wave in Mexico.

• International Journal of Contents
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• v.8 no.3
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• pp.7-11
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• 2012

This study examines A-list users in the Twitter network of National Assembly members in South Korea. An examination of some socio-geographic characteristics of these A-list users indicates that the distribution of these users in terms of their geographic location and social status can be understood in the context of the Korean social structure. In addition, an examination of Tweets posted by these users shows that half of these users had negative attitudes toward the current administration and that some Tweets contained emotional terms.

• Asia pacific journal of information systems
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• v.25 no.2
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• pp.239-264
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• 2015

This study aims to investigate the effect of message content and source user identity on information diffusion in Twitter networks. For the empirical study, we collected 11,346 tweets pertaining to the three major mobile telecom carriers in Korea for three months, from September to December 2011. These tweets generated 59,111 retweets (RTs) and were retweeted at least once. Our analysis indicates that information diffusion in Twitter in terms of RT volume is affected primarily by the type of message content, such as the inclusion of corporate social responsibility activities. However, the effect of message content on information diffusion is heterogeneous to the identity of the information source. We argue that user identity affects recipients' perception of the credibility of focal information. Our study offers insights into the information diffusion mechanism in online social networks and provides managerial implications on the strategic utilization of online social networks for marketing communications with customers.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.233-243
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• 2015

A hotspot is a spatial pattern that properties or events of spaces are densely revealed in a particular area. Whereas location information is easily captured with increasing use of mobile devices, so is not our emotion unless asking directly through a survey. Tweet provides a good way of analyzing such spatial sentiment, but relevant research is hard to find. Therefore, we analyzed hotspots of emotion in the twitter using spatial autocorrelation. 10,142 tweets and related GPS data were extracted. Sentiment of tweets was classified into good or bad with a support vector machine algorithm. We used Moran's I and Getis-Ord $G_i^*$ for global and local spatial autocorrelation. Some hotspots were found significant and drawn on Seoul metropolitan area map. These results were found very similar to an earlier conducted official survey of happiness index.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.538-547
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• 2013

In this paper, we propose a bias detection method that is based on personal and social opinions that express contrasting views on competing topics on Twitter. We used unsupervised polarity classification is conducted for learning social opinions on targets. The $tf{\cdot}idf$ algorithm is applied to extract targets to reflect sentiments and features of tweets. Our method addresses there being a lack of a sentiment lexicon when learning social opinions. To evaluate the effectiveness of our method, experiments were conducted on four issues using Twitter test collection. The proposed method achieved significant improvements over the baselines.

• The Journal of Information Systems
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• v.21 no.1
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• pp.69-91
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• 2012

The study investigates the media distinctiveness between twitter and other social media and describes how product information interpretation and responsiveness by internet users can be affected by the distinctive characteristics of twitter and blog media. The characteristics include relationship formation patterns among users, channel diversity, immediateness of information communication, information flow within media, media credibility, and management cost. Specifically, we statistically tested whether these characteristics are meaningfully differentiated by users. Results also showed that users perceived product information processing level and product evaluation direction differently based on these media characteristics. The current findings can serve as a pioneering work to provide a theoretical framework for examining social media characteristics and their impacts on consumer perception. In addition, this study practically suggests that marketers and network managers need to use differentiated communication strategies for twitters as a marketing strategic option.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.179-182
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• 2010

트위터, 페이스북 등의 소설 네트워크가 이슈가 되는 사건에 의견을 표시하는 수단으로 많이 활용되고 있다. 본 논문에서는 이슈 키워드 추출 및 트위터와 유투브에 기반한 실시간 검색 시스템을 구현한다. 본 시스템에서는 가장 최근 신문 기사들의 제목과 스니핏을 이용하여 이슈가 되는 키워드를 실시간으로 추출하여 사용자들에게 보여주고 트위터와 유투브 OpenAPI를 이용하여 추출된 키워드에 대한 컨텐츠들을 실시간으로 사용자들에게 보여준다, 본 시스템을 통해서 이슈가 되는 사건에 대한 실시간 반응을 찾을 수 있다.

• East Asian Economic Review
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• v.23 no.2
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• pp.149-168
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• 2019

Public information arrivals and their immediate incorporation in asset price is a key component of semi-strong form of the Efficient Market Hypothesis. In this study, we explore the impact of public information arrivals on cryptocurrency market via Twitter posts. The empirical analysis was conducted through various methods including Kapetanios unit root test, Maki cointegration analysis and Markov regime switching regression analysis. Results indicate that while in bull market positive public information arrivals have a positive influence on Ripple's value; in bear market, however, even if the company releases good news, it does not divert out the Ripple from downward trend.

• Journal of Public Diplomacy
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• v.1 no.1
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• pp.36-56
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• 2021

The Amazon fires in summer 2019 triggered an incendiary Twitter debate between French president Emmanuel Macron and Brazilian president Jair Bolsonaro that engaged political leaders, celebrities, and audiences worldwide. Currently, diplomats-in-chief connect to the global public through completely open debates, often without proper advice from foreign-affairs ministers, which may result in misunderstandings and conflicts among world leaders. Hence, this study argues that these interactions must be supported by Nicholas Cull's seven lessons in public diplomacy. The main topic on hand is presidential public diplomacy performed through digital means in cyberspace. Thus, after distinguishing cyberspace, digital diplomacy, and cyberdiplomacy, the literature review focuses on presidential public diplomacy, presidential diplomacy on Twitter, and Cull's seven lessons. Subsequently, the case study method provides a snapshot of the debate between Macron and Bolsonaro over the Amazon fires. This study concludes by answering the research question and indicating grist for the mill with regard to future developments.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.112-119
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• 2021

Social media has become a global means of communication in people's lives. Most people are using Twitter for communication purposes and its inappropriate use, which has negative effects on people's lives. One of the widely common misuses of Twitter is cyberbullying. As the resources of dialectal Arabic are rare, so for cyberbullying most people are using dialectal Arabic. For this reason, the ultimate goal of this study is to detect and classify cyberbullying on Twitter in the Arabic context in Saudi Arabia. To help in the detection and classification of tweets, Pointwise Mutual Information (PMI) to generate a lexicon, and Support Vector Machine (SVM) algorithms are used. The evaluation is performed on both methods in terms of the F1-score. However, the F1-score after applying the PMI is 50%, while after the SVM application on the resampling data it is 82%. The analysis of the results shows that the SVM algorithm outperforms better.