• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.983-992
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• 2012

Social indicator survey can identify the state of society as a whole. When we create a policy, social indicator survey can reflect the public opinion of the region. Social indicator survey is an important measure of social change. Social indicator survey has been conducted in many municipalities (Seoul, Incheon, Busan, Ulsan, Gyeongsangnamdo, etc.). But, the result of social indicator survey analysis is mainly the basic statistical analysis. In this study, we propose a new data mining methodology for effective analysis. We propose a 3-step complex data mining in society indicator survey. 3-step complex data mining uses three data mining method (intervening association rule, clustering, decision tree).

• Korean Journal of Cognitive Science
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• v.31 no.3
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• pp.55-76
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• 2020

Sociability is an inherent part of human life and also possesses an important value as a comprehensive ability. While the lack of sociability has been pointed out as a representative problem of game use in general, this paper analyzed studies on the relationship between online video games and social competence. In this field, the view that the relationship in the online game may replace or complement the actual relationship and will potentially hinder the development of sociability currently faces a conflict with the opinion that online video games may not directly have a negative effect on sociability but rather result in a positive outcome by providing a social learning space. In a large scale survey that measured the use of online games, psychological characteristics, and social competence, no distinct relationship between game use and degradation of sociability was observed. Based on this analysis, we suggest that efforts are necessary to break away from the stereotype that online game play may cause a decline in sociability and to improve the validity of related research.

• The Journal of Information Systems
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• v.26 no.4
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• pp.419-442
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• 2017

Purpose The purpose of this study is to explore the pattern of user response and it's duration time through social media content response analysis. We also analyze the characteristics of content quality factors which are associate with the user response pattern. The analysis results will provide some implications to develop strategies and schematic plans for the operator of regional marketing on the SNS. Design/methodology/approach This study used mixed methods to verify the effects and responses of social media contents on the users who have concerns about regional events such as local festival, cultural events, and city tours etc. Big data analysis was conducted with the quantitative data from regional government SNSs. The data was collected through web crawling in order to analyze the social media contents. We especially analyzed the contents duration time and peak level time. This study also analyzed the characteristics of contents quality factors using expert evaluation data on the social media contents. Finally, we verify the relationship between the contents quality factors and user response types by cross correlation analysis. Findings According to the big data analysis, we could find some content life cycle which can be explained through empirical distribution with peak time pattern and left skewed long tail. The user response patterns are dependent on time and contents quality. In addition, this study confirms that the level of quality of social media content is closely relate to user interaction and response pattern. As a result of the contents response pattern analysis, it is necessary to develop high quality contents design strategy and content posting and propagation tactics. The SNS operators need to develop high quality contents using rich-media technology and active response contents that induce opinion leader on the SNS.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.297-317
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• 2015

Although social media is recognized as a Web service that allows people to share and utilize their own opinion, experience and information, it is also faced with some problems such as indiscreet information creation and sharing, which resulted in the decline of reliability of information. This research statistically analyzed the effect of social media on information sharing. As a result, social media is an efficient information tool that can support information sharing and allow people to get feedback from other people. It also brought a positive effect on the entire information behavior. In contrast, it still has weaknesses in the utilization of information, including the decline of information reliability and excessive creation of inaccurate information. Eventually, social media is an information tool that can support the diversification of information and also incurs the decline of reliability of information.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.541-548
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• 2016

The use of social media is already essential as a source of information for listening user's various opinions and monitoring. We define social 'risks' that issues effect negative influences for public opinion in social media. This paper aims to discriminate various linguistic features and reveal their effects for building an automatic classification model of social risks. Expecially we adopt a word embedding technique for representation of linguistic clues in risk sentences. As a preliminary experiment to analyze characteristics of individual features, we revise errors in automatic linguistic analysis. At the result, the most important feature is NE (Named Entity) information and the best condition is when combine basic linguistic features. word embedding, and word clusters within core predicates. Experimental results under the real situation in social bigdata - including linguistic analysis errors - show 92.08% and 85.84% in precision respectively for frequent risk categories set and full test set.

• Korean Journal of Social Welfare Studies
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• v.49 no.1
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• pp.163-190
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• 2018

The citizen-initiative has been frequently mentioned in community building project which is being promoted by Seoul City. The citizen-initiative has become an important concept in the direction of community welfare efforts. However, this concept has not been defined clearly in social welfare. In this context, the purpose of this study is to find how the practitioners of the social welfare practice field recognize the concept of citizen-initiative. In this study, concept mapping method was used to generate 59 statements about the citizeninitiative in 10 social workers in Seoul. Multidimensional scaling analysis and hierarchical cluster analysis are used to do mapping and grouping the 59 statements. The results are as follows. A total of 6 categories were derived. The six categories are named "Inducement of Participation", "Practice", "Procedure", "Awareness and Interest extension", "Expression of Opinion", "Attitude and Emotion". "Practice" category was revealed as a core category in the concept of citizen-initiative. This study is meaningful as a first step to discuss "what is the citizen-initiative?" and to make consensus in social welfare academic area and practice field.

• Journal of The Korean Association For Science Education
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• v.27 no.7
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• pp.559-569
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• 2007

The purpose of this case study is to analyze the characteristics of social interaction according to gender-grouping in the application of LTTS program. For this study, mixed-gender group A (one boy and three girls or one girl and three boys), mixed-gender group B (two boys and two girls) and same-gender group (4 boys or 4 girls) were formed. Social interactions during group discussions were audio-/video-taped. Social interactions between one boy and one girl in each group were analyzed. The type of social interactions were classified as cognitive and affective interactions. The boy and the girl in the same gender group tended to make suggestion actively, but sometimes they ordered peers to participate or prevented peers from participating. On the other hand, they didn't tend to make suggestion about problem-solving in mixed-gender group A, but made suggestion against peer's opinion using appropriate reasons. The frequency of affective interactions in the mixed gender group B were higher.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Gifted/Talented Education
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• v.27 no.1
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• pp.97-118
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• 2017

This paper aims to provide reference model for directions and objectives of Software(SW) gifted education. In order to achieve the goals introduced above, we conducted the research in the following steps. First, we selected the concept of ICT-based creative talented person as a base model to establish the concept of SW gifted person. The selected base model composed three core competencies which were 'knowledge and technology competencies', 'synthesizing and creativity competencies', and 'personality competencies'. Second, we developed survey tools, like questionnaires, to investigate participant's recognition of SW gifted person. The survey tools composed three components 'computational thinking', 'entrepreneurship', and 'social responsibility'. Each of the components composed seven elements. Third, after selecting the opinion poll participants as an elementary school teacher, we surveyed opinion polling. By selecting an elementary school teacher as the opinion poll participants, we wanted to identify theirs ' opinions which are thought to be the starting point for gifted education. To survey we developed on-line survey system by using Google functions. Fourth, we analyzed the collected opinion data. To identify we summarized and synthesized participant's opinions that average values and agreement level by using frequency analysis. Also, in order to compare opinions that average values and agreement level based on whether or not participant's various experiences and competencies we computed t-value, F-value, and ${\chi}^2$ verification.