• The Journal of Information Systems
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• v.26 no.4
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• pp.327-353
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• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.814-818
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• 2010

Online social networking services help people to migrate social networks from offline to online. Twitter, which has achieved incredible growth, showed that an online social networking service without offline bases can become large and successful. In this paper, we propose a twitter friend notification service using user‘s twitter messages and place recognizing technology. When there is a friend in user‘s nearby place, the service notifies the information to the users. Through the friend notification service, a user can easily extend his online social network to offline.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.131-145
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• 2014

This study is an empirical analysis on the relationship between social presence and online community users' continuance intention. Based on Bhattacherjee(2001)'s expectation-confirmation model (ECM) of IT continuance model, we test the influence of social presence on one's intention to continue using online communities. This study sampled 132 online community users. Research hypotheses are tested using the structural equation modelling(SEM) approach. The results of this study demonstrate that user satisfaction is influenced by perceived usefulness and perceived enjoyment. But, the confirmation of expectation did not affect user satisfaction. And, social presence has direct effects on perceived usefulness and perceived enjoyment. Further, social presence has a positive effect on users' continuance intention through mediating effect of perceived usefulness. This study suggests that perceived usefulness should be taken into account when carrying out the operating strategy of online communities.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.97-111
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• 2016

In the creative economy paradigm, motivation of the opportunity based startup is a continuous concern to policy-makers. Recently, bigdata anlalytics challenge traditional methods by providing efficient ways to identify social trend and hidden issues in the public sector. In this study the authors introduce a case study using social bigdata analytics for conducting policy analysis. A semantic network analysis was employed using textual data from social media including online news, blog, and private bulletin board which create buzz on the startup business. Results indicates that each media has been forming different discourses regarding government's policy on the startup business. Furthermore, semantic network structures from private bulletin board reveal unexpected social burden that hiders opening a startup, which has not been found in the traditional survey nor experts interview. Based on these results, the authors found the feasibility of using social bigdata analysis for policy-making. Methodological and practical implications are discussed.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.601-605
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• 2009

Recently, photo sharing and publishing based Social Network Sites(SNSs) are increasingly attracting the attention of academic and industry researches. Millions of users have integrated these sites into their daily practices to communicate with online people. In this paper, we propose an efficient face annotation and retrieval system under SNS. Since the system needs to deal with a huge database which consists of an increasing users and images, both effectiveness and efficiency are required, In order to deal with this problem, we propose a face annotation classifier which adopts an online learning and social decomposition approach. The proposed method is shown to have comparable accuracy and better efficiency than that of the widely used Support Vector Machine. Consequently, the proposed framework can reduce the user's tedious efforts to annotate face images and provides a fast response to millions of users.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.95-101
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• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.39-59
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• 2024

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.18-26
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• 2011

Social network services whose users increase rapidly is the online services that reflect social network. They are used for various purposes such as strategy of election, commercial advertisement and marketing, educational information sharing and exchange of medical knowledge and opinions. These services make users form social networks with other users who have common interests and expand their relationships by releasing their personal information and utilizing other users' social networks. However, the social network services based on open and sharing of information raise various security threats such as violation of privacy and phishing. In this paper, we propose a group key management scheme and protocols using key rings to protect communication of small groups in social network services.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.127-135
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• 2021

The goal of this study was to explore the use of multiple SNS platforms and determine whether the number of used platforms affects one's online self-presentations across the said platforms and if there is any difference in one's online and offline self-presentations based on how many SNS platforms are used. This work studied online self-presentations, compared the on/offline ones and tried to find out if the inconsistencies of one's own (observer's) self-presentations both online (across platforms) and on/offline affected the observer's impression formation (likability, trustworthiness and hypocrisy) of others. The study also aimed to find out if the impression of the others' inconsistency both online and offline would differ based on the level of intimacy between the observer and the discussant. Three levels of intimacy were studied in order to do this: friends, acquaintances and strangers (online-only friends). The results showed that the more platforms people used the more inconsistent their online self-presentations got. Even though the results of the study showed barely significant relationship between the number of SNS accounts and one's online and offline self-presentation, and partial connection between observer's inconsistent self-presentations and impression formation of others, interestingly enough, the results managed to find significant differences between the impressions based on the level of intimacy between the observer and the discussants.