• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.434-441
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• 2016

Using bigdata analysis procedures, the present study sought to review and explore the various aspects of self-disclosure and boundary impermeability of worldwide twitter users. A total of 415 million tweets issued by 54 million users were collected during 6 months and the users of top 10 languages were investigated. And the effect of languages of twitter users on the boundary impermeability, disclosure rate of user profile, profile image, geographical information, URL in profile and user description were analyzed in this study. The results showed that the boundary impermeability and all the self-disclosure rates of twitter users (profile, profile image, geographical information, URL in profile, user description) were significantly (p<0.001) different among language groups of users. The self-disclosure rates and the average points of Portuguese, Indonesian and Spanish users were higher than those of Arabic, Japanese, Turkish and Korean users. The results also showed a positive relationship between boundary impermeability and the number of tweets (including retweets) issued by each users.

• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.455-465
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• 2016

The purpose of this study was to examine the effect of twitter influenctials on users' willingness on express opinions. Based on the spiral of silence and corrective action hypothesis, the contradictory hypotheses were drawn. The online expeiment was conducted to verify which hypothesis is valid. Participant were assigned to the four experimenatal conditons: a famous influential's twit which agrees with the issue; a famous influential's twit which disagrees with the issue; an ordinary influential's twit to agree with the issue; an ordinary influential's twit to disagree with the issue. Results showed that opinion congruency with a twitter influential did not influence participants' willingness to express opinion online and offline, but the interaction effect with the type of influentials was found. Opinion dissonance with an ordinary tiwtterian increased willingness to express opinion. The findings suggest that twitter influentials, particulary an ordinary influentials with differnet opinions, can motivate users to express their own opinion.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.341-348
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• 2014

The advance in web accessibility with dissemination of smart phones gives rise to rapid increment of users on social network platforms. Many research projects are in progress to detect events using Twitter because it has a powerful influence on the dissemination of information with its open networks, and it is the representative service which generates more than 500 million Tweets a day in average; however, existing studies to detect events has been used TFIDF algorithm without any consideration of the various conditions of tweets. In addition, some of them detected predefined events. In this paper, we propose the RTFIDF VT algorithm which is a modified algorithm of TFIDF by reflecting features of Twitter. We also verified the optimal section of TF and DF for detecting events through the experiment. Finally, we suggest a system that extracts result-sets of places and related keywords at the given specific time using the RTFIDF VT algorithm and validated section of TF and DF.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.69-74
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• 2012

Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.81-91
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• 2019

Recently, social network service (SNS) is actively used by public. Among them, Twitter has a lot of tweets including sentiment and it is convenient to collect data through open Aplication Programming Interface (API). In this paper, we analyze social issues and suggest the possibility of using them in marketing through sentimental information of users. In this paper, we collect twitter text about social issues and classify as positive or negative by sentiment classifier to provide qualitative analysis. We provide a quantitative analysis by analyzing the correlation between the number of like and retweet of each tweet. As a result of the qualitative analysis, we suggest solutions to attract the interest of the public or consumers. As a result of the quantitative analysis, we conclude that the positive tweet should be brief to attract the users' attention on the Twitter. As future work, we will continue to analyze various social issues.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.174-186
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• 2019

Twitter is very popluar with users who desire social interaction as it is a highly effective method of communicating compared to traditional communication platforms; and thus has garnered considerable interest from the academic community. This research reveals how election results can be predicted by the factors of total volume of messages, positive messages and negative messages tweeted about a candidate. Social matrix analysis revealed that the quantity of twitter messages was a strong predictor of election results in the 18th presidential election in Korea. In addition, more positive messages than negative messages about a candidate from twitter users recorded better results in the election. This research found that the total quantity of messages, positive messages, and negative messages as key factors for predicting election result. Future studies should investigate other SNS platforms to discover what is the most effective communication strategy on each platform.

• Journal of Korean Academy of Oral Health
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• v.41 no.2
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• pp.154-161
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• 2017

Objectives: The purpose of this study was to investigate changes in public recognition of parabens on Twitter and the research status of parabens related to toothpaste. Methods: Tweet information between 2010 and October 2016 was collected by an automatic web crawler and examined according to tweet frequency, key words (2012-October 2016), and issue tweet detection analyses to reveal changes in public recognition of parabens on Twitter. To investigate the research status of parabens related to toothpaste, queries such as "paraben," "paraben and toxicity," "paraben and (toothpastes or dentifrices)," and "paraben and (toothpastes or dentifrices) and toxicity" were used. Results: The number of tweets concerning parabens sharply increased when parabens in toothpaste emerged as a social issue (October 2014), and decreased from 2015 onward. However, toothpaste and its related terms were continuously included in the core key words extracted from tweets from 2015. They were not included in key words before 2014, indicating that the emergence of parabens in toothpaste as a social issue plays an important role in public recognition of parabens in toothpaste. The issue tweet analysis also confirmed the change in public recognition of parabens in toothpaste. Despite the expansion of public recognition of parabens in toothpaste, there are only seven research articles on the topic in PubMed. Conclusions: The general public clearly recognized parabens in toothpaste after emergence of parabens in toothpaste as a social issue. Nevertheless, the scientific information on parabens in toothpaste is very limited, suggesting that the efforts of dental scientists are required to expand scientific knowledge related to parabens in oral hygiene measures.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.58-64
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• 2018

This main purpose of the study is to identify social network of communicators sharing information on Bokjiro for publicizing welfare policy. This study employs NodeXL pro to understand networks and their role in the social network. The data for social network analysis was collected from Twitter for a week. The result of the analysis shows that the social network of communicators on Bokjiro does not have many nodes. It also has an independent network with high possibility of information distortion. Little communicators have controlling power in information flow in one way of communication. According to the result, it is not effective for marketing strategy of welfare policy in providing online information through Bokjiro. The study suggests that the government should use the transactional approach to marketing based on agent-oriented activity focusing on the exchange relationship between information providers and demanders in an age of networked intelligence.

• Journal of Information Technology Applications and Management
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• v.19 no.2
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• pp.197-215
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• 2012

SNS has been recognized as a means of expanding social capital by promoting interaction and efficient communication among users. On the other hand, there are serious concerns on negative side of social network which is often called epidemics. Trust plays a critical role in controlling the spread of distorted information and vicious rumor as well as reducing uncertainties and risk from unreliable users in social network. This study focuses on what the antecedents of interpersonal trust are in social network. We performed online survey from 252 Twitter users and tested candidate antecedents which are chosen from previous literature. As a result, propensity to trust of trustor, ability and sincerity of trustee, intimacy between trustor and trustee significantly affected to the interpersonal trust in Twitter.

• Korean Journal of Mathematics
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• v.22 no.3
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• pp.529-552
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• 2014

As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.