• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.343-344
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• 2018

온라인 쇼핑몰에서 소비자들이 원하는 상품을 노출시켜 정보를 제공하기 위해서는 상품의 트렌드 분석에 대한 연구가 필요하다. 본 논문에서는 대량의 SNS 데이터와 서비스 내 사용자 데이터를 결합하여 보다 효율적인 상품 트렌드 분석 기법을 제안한다. 온라인 소셜 네트워크의 대중화로 소비자들은 시공간에 구애받지 않고 상품에 대한 정보를 SNS로 교류할 수 있다. 제안하는 기법은 이 과정에서 발생한 SNS 데이터와 사용자 성향 데이터에 시간 속성을 고려하여 상품 트렌드를 분석한다.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.97-108
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• 2019

Purpose: The purpose of this study is to propose a new Risk Priority Number(RPN) evaluation method which analyzes value of product functions by mining customer opinions in Social Network Service(SNS). Methods: A traditional RPN is measured by three evaluation standards (Severity, Occurrence, Detection) which are analyzed by manufacturing engineers and researchers. On the other hand, these standards are analyzed by customers' viewpoints through SNS opinion mining in this research. In order to extract customer feedbacks from textual data sets, the methodology in this paper implies natural language processing, hereby collecting product related data sets and analyzing the opinions automatically. An emotional polarity of an opinion indicates severity, while the number of negative opinion shows occurrence, and the entire number of customer opinion refers to detection. Results: The results of this study are as follows; As a result of the CRPN evaluation, it is confirmed that the features evaluated as risky are highly likely to be improved in the next series. Therefore, CRPN is an effective risk assessment model that reflects customer feedback. Conclusion: Reflecting customer feedback is a useful tool for risk assessment of the product as well as for developing new products and improving existing products.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1636-1637
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• 2015

소셜 네트워크 서비스(SNS)에서는 사용자들의 친한 관계를 나타내는 여러 가지 특징을 발견할 수 있다. 본 논문에서는 트위터 이고-네트워크(ego-network) 데이터를 이용한 분석 실험을 통해 유저 간 친밀한 정도를 나타내는 여러 특징들과 관심사 유사도의 상관관계를 밝힌다.

• Korean Security Journal
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• no.29
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• pp.301-336
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• 2011

This exploratory study aims to review the risks and threats of social network services(SNSs), particularly focusing upon the policing perspective. This paper seeks to acknowledge the present risk/danger of SNSs and the very significance of establishing a strategic framework to effectively prevent and/or control criminal misuse of SNSs. This research thus advocates that proactive study on security issues and criminal aspects of SNSs and preventive countermeasures can play a significant role in policing the networked society in the time of digital/internet age. Social network sites have been increasingly attracting the attention of entrepreneurs, and academic researchers as well. In this exploratory article, the researcher tried to define concepts and features of SNSs and describe a variety of issues and threats posed by SNSs. After summarizing existing security risks, the researcher also investigated both the potential threats to privacy associated with SNSs, such as ID theft and fraud, and the very danger of SNSs in case of being utilized by terrorists and/or criminals, including cyber-criminals. In this study, the researcher primarily used literature reviews and empirical methods. The researcher thus conducted extensive case studies and literature reviews on SNSs. The literature reviews herein cover theoretical discussions on characteristics, usefulness, and/or potential danger/harm of SNSs. Through the literature review, the researcher also concentrated upon being able to identify a strategic framework for law enforcement to effectively prevent criminal misuse of SNSs The limitation of this study can be lack of statistical data and attempts to examine previously un-researched area in the field of SNS and its security risks and potential criminal misuse. Thus, to supplement this exploratory study, more objective theoretical models and/or statistical approaches would be needed to provide law enforcement with sustainable policing framework and contribute to suggesting policy implications.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.117-139
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• 2013

Due to the recent development of SNS(Social Network Service) which is represented by Twitter and Facebook, social commerce market which combines on-line shopping mall and SNS is expanding. With increasing speed of its market, this study is to focus on detecting the crucial variables promoting the use intention, which results in the sustainable competitive advantage in social commerce market. This research empirically analyzed the use intention of social commerce. Also, this study explores the moderating effects of gender in explaining intention to use social commerce. A technology acceptance model is applied for pinpointing the antecedents of intention to use social commerce and for revealing cross-gender differences. The results from a survey of 452 participants reveal that the effects of perceived risk, perceived usefulness, ease for use and subjective norm on intentions to use social commerce differ across gender. Findings imply that the risk-free quality and portrayal of in-group or out-group situations of social commerce need to be communicated to female consumers, along with the social norms of using social commerce. When targeting male consumers, the technological features illustrating the usefulness of social commerce should be focused. This study will provide diverse implications companies providing social commerce services.

• The Journal of Information Systems
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• v.32 no.4
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• pp.69-94
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• 2023

Purpose Social networking service (SNS) platforms employ distinct networking strategies to meet the varying needs of their users, resulting in divergent sets of technological functionalities offered by each platform. Consequently, unique features on various SNSs give rise to distinct social issues. Moreover, the available technical coping mechanisms for users vary significantly across platforms. Design/methodology/approach Therefore, this study analyzes the factors affecting technical coping intention based on technical functions of SNSs for users exposed to cybercrime, such as sexual harassment. We divide coping intention into active and passive coping intention. Furthermore, this research focuses on trust as an antecedent of coping intention and verifies how human and system-like trust affects two coping intentions in different directions. Findings Findings reveal that system-like trust significantly affects both active and passive coping intention as a belief in whether the technology will work properly. However, in the case of human-like trust, trust in the platform provider was found to negatively affect passive coping, which is considered unsocialized behavior on SNS platforms. Therefore, both human-like and system-like trust for the platform must be appropriately applied to cope with the problem while activating the platform.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.388-397
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• 2012

Recently, one of the received attraction fields in web based service is 'Human Relationship Service' that is called SNS. This relationship map service is able to deliver information to user more easily and visually because it is intuitive data that is linked with offline real world. While past map service put physical real information in the map, present map service is evolving into new communicative platform that expresses social relationship beyond simple search platform that shows real world. In this paper, we propose smart social grid system using sketch map that is based on online map service structure. This system has features such as standardized interface provision for various SNS, use to governance hub tool in case of establishing a personal network through expanded social grid, a role of bridge to mashup software linked with other SNS, user environment construction that reproduces social grid data, and the faster service setup by improved search technology.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1014-1028
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• 2017

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.33-42
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• 2016

Recently, an environment in which public opinions are expressed about various areas is expanded around SNSs or internet potals, thus, opinion documents get bigger rapidly. Under these circumstances, it is essential to utilize automatic summarization techniques for understanding whole contents of large opinion documents. However, it is hard to summarize efficiently those documents with traditional text summarization technologies since the documents include subject expressions as well as features of targets objects. Proposed method in this paper defines features of opinion documents, and designed to retrieve representative sentences expressing opinions of those features. In addition, through experiments, we prove the usefulness of proposed method.