• KIPS Transactions on Computer and Communication Systems
• /
• v.5 no.9
• /
• pp.229-236
• /
• 2016

In the past, crime prediction methods utilized previous records to accurately predict crime occurrences. Yet these crime prediction models had difficulty in updating immense data. To enhance the crime prediction methods, some approaches used social network service (SNS) data in crime prediction studies, but the relationship between SNS data and crime records has not been studied thoroughly. Hence, in this paper, we analyze the relationship between SNS data and criminal occurrences in the perspective of crime prediction. Using Latent Dirichlet Allocation (LDA), we extract tweets that included any words regarding criminal occurrences and analyze the changes in tweet frequency according to the crime records. We then calculate the number of tweets including crime related words and investigate accordingly depending on crime occurrences. Our experimental results demonstrate that there is a difference in crime related tweet occurrences when criminal activity occurs. Moreover, our results show that SNS data analysis will be helpful in crime prediction model as there are certain patterns in tweet occurrences before and after the crime.

• Journal of The Korean Association of Information Education
• /
• v.25 no.2
• /
• pp.289-300
• /
• 2021

SNS data was collected and analyzed by topic modeling techniques to examine recent trends in information education. By deriving keywords and topics for SW education and AI education, we not only attempted to discover insights ahead of the next revised curriculum but also suggested directions. According to the SNS data analysis, the contents of human resource development for software and the instructional method in schools are indicated as a high requirement. Meanwhile, SW education should be conducted through a separate curriculum from elementary school, and this was consistent with the opinion that it is necessary to be organized as a required subject. There was an opinion to support the schools since AI education is newly introduced in next revised national curriculum. The trends in SW education and AI education which are observed through SNS data analysis could be concluded to conduct the substantial operation of information education and curriculum organization.

• Journal of Digital Convergence
• /
• v.11 no.1
• /
• pp.237-242
• /
• 2013

SNS is emerging as an academic and social interest, as Facebook and Twitter are developed explosively. But, SNS has a problem of exposing user's privacy because it is originated by exchanging user's personal information and opinion. This paper proposes SNS user privacy protecting model using data separation and false data information instead of blocking which is using to protect user's personal privacy. The proposed model do not let the third party extract precise information after collecting user's context information by adding false information to separated context information. Also, it gets user's agreement beforehand if SNS service provider uses user's information not to be used illegally by the third party.

• Convergence Security Journal
• /
• v.22 no.1
• /
• pp.55-60
• /
• 2022

This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

• The KIPS Transactions:PartD
• /
• v.19D no.4
• /
• pp.271-280
• /
• 2012

Currently, contents generated by SNS services are increasing exponentially, as the number of SNS users increase. The SNS is commonly used to post personal status and individual interests. Also, the SNS is applied in socialization, entertainment, product marketing, news sharing, and single person journalism. As SNS services became available on smart phones, the users of SNS services can generate and spread the social issues and controversies faster than the traditional media. The existing indexing methods for web contents have limitation in terms of real-time indexing for SNS contents, as they usually focus on diversity and accuracy of indexing. To overcome this problem, there are real-time indexing techniques based on RDBMSs. However, these techniques suffer from complex indexing procedures and reduced indexing targets. In this regard, we introduce the TK-Indexing method to improve the previous indexing techniques. Our method indexes the generation time of SNS contents and keywords by way of NoSQL to indexing SNS contents in real-time.

• Journal of the Korea society of information convergence
• /
• v.7 no.2
• /
• pp.49-57
• /
• 2014

Recently, opinion mining is introduced to extract useful information from SNS data and to evaluate the true intention of users. Opinion mining are required several efficient techniques to collect and analyze a large amount of SNS data and extract meaningful data from them. Therefore in this paper, we propose a parallel HDFS(Hadoop Distributed File System) and emotion functions based on Mapreduce to extract some emotional information of users from various unstructured big data on social networks. The experiment results have verified that the proposed system and functions perform faster than O(n) for data gathering time and loading time, and maintain stable load balancing for memory and CPU resources.

• Information Systems Review
• /
• v.20 no.2
• /
• pp.63-85
• /
• 2018

Emerging hotspot and trendy areas are formed into alleys and blocks with the help of viral effects among social network services (SNS) users called "Golmogleo." These users search for every corner of the alleys to share and promote their own favorite places through SNS. An analysis of hot places is limited if it is only based on macroeconomic indicators such as commercial area data published by national organizations, large-scale visiting facilities, and commuter figures. Careful analyses based on consumers' actual activities are needed. This study develops a "social big data analysis methodology" using Instagram data, which is one of the most popular SNSs suitable to identify recent consumer trends. We build a spatial analysis model using Local Moran's I. Results show that our model identifies new trend zones on the basis of posting data in Instagram, which are not included in the commercial information prepared by national organizations. The proposed analysis methodology enables better identification of the latest trend areas formulated by SNS user activities. It also provides practical information for start-ups, small business owners, and alley merchants for marketing purposes. This analytical methodology can be applied to future studies on social big data analysis.

• Journal of Digital Convergence
• /
• v.10 no.4
• /
• pp.257-263
• /
• 2012

Smart phones become popular all over the world recently. At the same time, demand of various additional services, such as SNS by utilizing low-cost reliable Wi-Fi network and position information, is expected to keep growing. In this paper, Implementation of SNS based on an Open Wi-Fi & Position Information was proposed. This service is achieved by constructing an Open Wi-Fi network based on a built AP access information database. And in order to provide durable connection in mobile environment, RSS detect AP switching module and mobile IP are utilized in the proposed service. Furthermore, with the utilization of GPS information of AP, AP providers could delivery various information such as advertisements, promotion events. In addition, it is possible for AP users to communicate with each other, thus a position information based SNS was also proposed in this paper.

• Journal of Information Technology Applications and Management
• /
• v.21 no.1
• /
• pp.35-51
• /
• 2014

The popularity of Social Network Service (SNS) providing web sites has increased continuously by using a variety of mobile devices. The study results show that security of SNS, efficiency of SNS, safety, empathy of SNS quality, easy of use of SNS, assurance of SNS, service variety of SNS are having positive impact to some degree on the user satisfaction. Further, the user satisfaction of SNS users have a positive impact on the continuance usage intention of SNS users. This results show that various SNS qualities are necessary to actively explore and obtain further information that users intend to find, while they are insufficient in function to provide the information other users require or exchange information with other users through the SNS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.438-440
• /
• 2014

Recently it is used commercially to actively move the data from the SNS service. Therefore, we propose a method that can accurately analyze the information related to the reputation of companies and products in real time SNS environment in this paper.Identify the relationship between words by performing morphological analysis on the text data gathered by crawling the SNS scheme. In addition, it shows the visualization to analyze statistically through a established emotional dictionary morphemes are extracted from the sentence. Here, if the extracted word is not exist in sentimental dictionary. Also, we propose the algorithm that add the word to emotional dictionary automatically.