• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.1
• /
• pp.61-71
• /
• 2014

Social networking services have changed the way people communicate. Rapid growth of information generated by social networking services requires effective search methods to give useful results. Over the last decade, social search methods have rapidly evolved. Traditional techniques become unqualified because they ignore social relation data. Existing social recommendation approaches consider social network structure, but social context has not been fully considered. Especially, the friend recommendation is an important feature of SNSs. People tend to trust the opinions of friends they know rather than the opinions of strangers. In this paper, we propose a levelized data processing method for social search in ubiquitous environment. We study previous researches about social search methods in ubiquitous environment. Our method is a new paradigm of levelelized data processing method which can utilize information in social networks, using location and friendship weight. Several experiments are performed and the results verify that the proposed method's performance is better than other existing method.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.3
• /
• pp.31-38
• /
• 2013

The evolution of the Web from Web 1.0 to Web 2.0 has brought up new platforms as SNSs(Social Network Service) that are used by users to articulate and manage their relationships. SNSs are an online phenomenon which has become extremely popular. A SNS essentially consists of a representation of each user, his/her social links, and a variety of additional services. SNSs are increasingly attracting the attention of academic and industry researchers. What makes SNS unique is that they have a relationship with friends. The friend recommendation is one important feature of social networking services. People tend to trust the opinions of friends they know rather than the opinions of strangers. In this paper, we propose an efficient data processing method for social data. We study previous researches about social score in social network service. Our ESS(Efficient Social Score) is computed by both friendship weight and score of a document that was tagged by a user's friends. Our experimental results also confirm that our method has good performance.

• Journal of Digital Convergence
• /
• v.11 no.5
• /
• pp.41-51
• /
• 2013

A lots of social data are distributed, utilized and opened through the social media. They have characterized effectiveness and pleasure of information to the media by social data but it is ignored about excessive exposure of information and damage from collective reply of personal attack type. In this paper, we study about analysis of opinion social data on the SNS (Social Network Service) by analyzing of collective damage reply. It is analysed by diverse measurement method for distribution and disuse of the amount of Buzz data that is analysed data from structured social network.

• Journal of applied mathematics & informatics
• /
• v.40 no.1_2
• /
• pp.289-303
• /
• 2022

This article examines the multidimensional index extraction method of the disability social security system based on data mining. While creating the data warehouse of the social security system for the disabled, we need to know the elements of the social security indicators for the disabled. In this context, a clustering algorithm was used to extract the indicators of the social security system for the disabled by investigating the historical dimension of social security for the disabled. The simulation results show that the index extraction method has high coverage, sensitivity and reliability. In this paper, a multidimensional extraction method is introduced to extract the indicators of the social security system for the disabled based on data mining. The simulation experiments show that the method presented in this paper is more reliable, and the indicators of social security system for the disabled extracted are more effective in practical application.

• Genomics & Informatics
• /
• v.18 no.2
• /
• pp.16.1-16.5
• /
• 2020

There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2,500 publication entries since 2014 that deal with analyzing social media data from Twitter and Reddit. However, the vast majority of those works do not share their code or data for replicating their studies. With minimal exceptions, the few that do, place the burden on the researcher to figure out how to fetch the data, how to best format their data, and how to create automatic and manual annotations on the acquired data. In order to address this pressing issue, we introduce the Social Media Mining Toolkit (SMMT), a suite of tools aimed to encapsulate the cumbersome details of acquiring, preprocessing, annotating and standardizing social media data. The purpose of our toolkit is for researchers to focus on answering research questions, and not the technical aspects of using social media data. By using a standard toolkit, researchers will be able to acquire, use, and release data in a consistent way that is transparent for everybody using the toolkit, hence, simplifying research reproducibility and accessibility in the social media domain.

• Asian Journal for Public Opinion Research
• /
• v.5 no.4
• /
• pp.285-301
• /
• 2018

There have been many studies that applied a data-driven analysis method to social media data, and some have even argued that this method can replace traditional polls. However, some other studies show contradictory results. There seems to be no consensus as to the methodology of data collection and analysis. But as social media-based election research continues and the data collection and analysis methodology keep developing, we need to review the key points of the controversy and to identify ways to go forward. Although some previous studies have reviewed the strengths and weaknesses of the social media-based election studies, they focused on predictive performance and did not adequately address other studies that utilized social media to address other issues related with public opinion during elections, such as public agenda or information diffusion. This paper tries to find out what information we can get by utilizing social media data and what limitations social media data has. Also, we review the various attempts to overcome these limitations. Finally, we suggest how we can best utilize social media data in understanding public opinion during elections.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.3
• /
• pp.603-608
• /
• 2005

The purpose of this qualitative case study is to understand how the idea of data view and information graphics is used in the social studios middle school textbooks. Data were collected through national curriculum documents and social studies middle textbooks for 7-9 grades. We set up three questions for this studies; what kinds of information graphics are used in the textbooks, how the graphics are organized in the social studies middle school, and how the 7th social studies curriculum is related with the 7th national mathematics curriculum. Through the data analysis, we found that 1) Photographs, illustrations, information maps, etc., are used and frequencies of their usages are in descending order, 2) double lines graphs, circle graphs, and stripe graphs nip often adopted for the comparison of populations, 3) the relation of the two subjects curricula is not so good, especially in the curriculum steps of information mads scatter diagrams, and comparison of populations. Finally we suggest that new web site of data view or information graphics be provided for two curricula, workshop of information graphics are needed for social studies teachers.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.12
• /
• pp.553-564
• /
• 2014

In this paper, we propose the system for automatic generation of issue analysis report based on social big data mining, with the purpose of resolving three problems of the previous technologies in a social media analysis and analytic report generation. Three problems are the isolation of analysis, the subjectivity of experts and the closure of information attributable to a high price. The system is comprised of the natural language query analysis, the issue analysis, the social big data analysis, the social big data correlation analysis and the automatic report generation. For the evaluation of report usefulness, we used a Likert scale and made two experts of big data analysis evaluate. The result shows that the quality of report is comparatively useful and reliable. Because of a low price of the report generation, the correlation analysis of social big data and the objectivity of social big data analysis, the proposed system will lead us to the popularization of social big data analysis.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.6
• /
• pp.851-859
• /
• 2017

Today, with the advent and development of Social Network Service (SNS), various types of information that have been difficult to observe have been pouring out. Recently, Vertical Social Networking Service (SNS), a service that shares specific interests with users' Vertical Social Networking Service) is emerging as a major research area. Especially, various human, social and spatial characteristics can be observed through geolocation data and social data collected through mobile GPS, and it is used in various studies. In this study, we analyze the social data collected through the image - based vertical SNS Instagram, and measure the user 's experience based on the social media based on the user' s spatial context. Therefore, in this study, we investigate what types of spatial patterns exist between experiential elements of sharing experiences and geographical characteristics through social data, and examine a new model of shared experience structure through extracted data.

• Journal of the Korean Society of Safety
• /
• v.32 no.3
• /
• pp.83-89
• /
• 2017

A new technology has provided the nation, industry, society, and people with innovative and useful functions. National economy and society has been improved through this technology innovation. Despite the benefit of technology innovation, however, since technology society was sufficiently mature, the unintended side effect and negative impact of new technology on society and human beings has been highlighted. Thus, it is important to investigate a risk of new technology for the future society. Recently, the risks of the new technology are being suggested through a large amount of social data such as news articles and report contents. These data can be used as effective sources for quantitatively and systematically forecasting social risks of new technology. In this respect, this paper aims to propose a data-driven process for forecasting and assessing social risks of future new technology using the text mining, 4M(Man, Machine, Media, and Management) framework, and analytic hierarchy process (AHP). First, social risk factors are forecasted based on social risk keywords extracted by the text mining of documents containing social risk information of new technology. Second, the social risk keywords are classified into the 4M causes to identify the degree of risk causes. Finally, the AHP is applied to assess impact of social risk factors and 4M causes based on social risk keywords. The proposed approach is helpful for technology engineers, safety managers, and policy makers to consider social risks of new technology and their impact.