• The Journal of Industrial Distribution & Business
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• v.5 no.1
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• pp.27-37
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• 2014

Purpose - This study analyzed the characteristics of social networking sites (SNSs) using related literatures, and researched the models discussed in precedent studies, to investigate the effects of SNS characteristics upon consumers'awareness, purchase intention, and recommendation. The purpose of the study was to investigate the use of SNSs as a marketing tool. Design, methodology, and approach - For an empirical analysis, the author distributed questionnaires online and offline, to verify the models and hypotheses. Respondents were persons aged 17 or older, who were frequent users of SNSs. The questionnaire survey was conducted for 11 days from September 30, 2013 to October 10, 2013. The author distributed 450 copies and received 430 responses. Finally, 412 copies were used for the analysis after excluding 18 copies having poor answers. Results - The findings about SNS users' behavior could be used as material in the future use of SNS as a marketing tool. Further, the study provided not only theories about SNS characteristics, but also variables and items that were verified during the empirical study. Conclusions - Further studies are needed to overcome the limitations and to establish various kinds of SNS marketing strategies in detail.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.673-682
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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. Unlike the face recognition environment addressed by existing works, face annotation problem under SNSs is differentiated in terms of daily updated images database, a limited number of training set and millions of users. Thus, conventional approach may not deal with these problems. In this paper, we proposed a face annotation method for sharing and publishing photographs that contain faces under a social network service using random projection, non-linear regression and representational state transfer. Our experiments on several databases show that the proposed method records an almost constant execution time with comparable accuracy of the PCA-SVM classifier.

• 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 Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Fashion & Textile Research Journal
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• v.19 no.6
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• pp.701-711
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• 2017

This research aimed to understand selfie behavior in social networking sites (SNSs). The research was conducted on the basis of the functional theories of attitude, verified self-presentation attitude, and self-expression attitude that affect selfie behaviors (i.e., taking selfies, posting selfies, and taking selfies for fashion product exposure). The moderating effect of satisfaction toward one's appearance was identified. The participants of the study were SNS users aged 20-30 years who had posted selfies in the past month. A survey was performed using an online panel of an international survey firm. The data were analyzed using hierarchical regression analysis on SPSS 22.0. Results corroborated that self-expression attitude affected the number of selfies taken but not the number of selfies posted and those uploaded for fashion product exposure. Self-presentation attitude exerted a significant effect on the number of selfies posted and those uploaded for fashion product exposure. When satisfaction toward one's appearance was high, self-presentation attitude increased the influence of the behaviors of posting selfies and uploading selfies for fashion product exposure. Self-expression attitude also significantly influenced the number of selfies taken due to the moderating effect of satisfaction toward one's appearance. This research was made meaningful by its quantitative analysis of selfie behavior in SNSs. The results confirmed the different functions of attitudes affecting selfie behavior. With the improved understanding of selfie behavior obtained from this research, Social Media marketing may be carried out in various industrial fields in the future.

• Information Systems Review
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• v.19 no.1
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• pp.25-48
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• 2017

As the number of Internet users has increased, the uses of social networking sites (SNSs) and online games have become universal activities across gender and ages. The extensive distribution and the usage of the Internet are beneficial to our society, but its adverse effects, such as Internet addiction, which refers to uncontrollable excessive Internet use, are becoming prevalent. Relevant social costs are also becoming troublesome. SNS and gaming addictions have negative effects on one's life, causing significant social problems. To illustrate different facets of these addictions, Problem Behavior Theory is adopted in the study. How self-esteem and perceived family environment affect SNS addiction and gaming addiction is addressed. The main subjects are Korean university students in their 20s who use SNS and play online games. The relationship between SNS addiction and gaming addiction is also addressed.

• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.59-77
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• 2015

Social networking sites (SNS), such as Facebook, provide abundant social comparison opportunities. Given the widespread use of SNSs, the purpose of the present study was to examine the impact of exposure to social media-based social comparison on user's negative emotions and discontinuous use intention on SNS. We present evidence that under the use of SNS, social comparison activities diverge into three patterns, with explicit self-evaluation desire made against similar target (lateral comparison), self-defense desire made against less fortunate target (downward comparison), and self-enhancement desire made with more fortunate target (upward comparison). Such social comparison processes frequently arise, as people are increasingly using on SNSs, the downward contacts ameliorating self-esteem with positive emotions, but the upward contacts and standard contacts with lateral status enabling a person to compare his or her situation with others and simultaneously increase negative emotions due to its differences with others. In other words, as people increasingly relying on SNSs for a variety of everyday tasks, they risk overexposure to upward or standard social comparison information that may have a cumulative detrimental impact on future intention on SNS use. This study with survey with 209 SNS users found that these negative emotions lead to negative fatigue (attitude) and then discontinuous use intention (behavior) on SNS. Our findings are among the first to explicitly examine discontinuous use intention on SNS using social comparison theory and our results are consistent with those of past research showing that upward social comparisons can be detrimental.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.45-67
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• 2016

Given the recent development of smart devices, users are producing, sharing, and acquiring a variety of information via the Internet and social network services (SNSs). Because users tend to use multiple media simultaneously according to their goals and preferences, domestic SNS users use around 2.09 media concurrently on average. Since the information provided by such media is usually textually represented, recent studies have been actively conducting textual analysis in order to understand users more deeply. Earlier studies using textual analysis focused on analyzing a document's contents without substantive consideration of the diverse characteristics of the source medium. However, current studies argue that analytical and interpretive approaches should be applied differently according to the characteristics of a document's source. Documents can be classified into the following types: informative documents for delivering information, expressive documents for expressing emotions and aesthetics, operational documents for inducing the recipient's behavior, and audiovisual media documents for supplementing the above three functions through images and music. Further, documents can be classified according to their contents, which comprise facts, concepts, procedures, principles, rules, stories, opinions, and descriptions. Documents have unique characteristics according to the source media by which they are distributed. In terms of newspapers, only highly trained people tend to write articles for public dissemination. In contrast, with SNSs, various types of users can freely write any message and such messages are distributed in an unpredictable way. Again, in the case of newspapers, each article exists independently and does not tend to have any relation to other articles. However, messages (original tweets) on Twitter, for example, are highly organized and regularly duplicated and repeated through replies and retweets. There have been many studies focusing on the different characteristics between newspapers and SNSs. However, it is difficult to find a study that focuses on the difference between the two media from the perspective of supply and demand. We can regard the articles of newspapers as a kind of information supply, whereas messages on various SNSs represent a demand for information. By investigating traditional newspapers and SNSs from the perspective of supply and demand of information, we can explore and explain the information dilemma more clearly. For example, there may be superfluous issues that are heavily reported in newspaper articles despite the fact that users seldom have much interest in these issues. Such overproduced information is not only a waste of media resources but also makes it difficult to find valuable, in-demand information. Further, some issues that are covered by only a few newspapers may be of high interest to SNS users. To alleviate the deleterious effects of information asymmetries, it is necessary to analyze the supply and demand of each information source and, accordingly, provide information flexibly. Such an approach would allow the value of information to be explored and approximated on the basis of the supply-demand balance. Conceptually, this is very similar to the price of goods or services being determined by the supply-demand relationship. Adopting this concept, media companies could focus on the production of highly in-demand issues that are in short supply. In this study, we selected Internet news sites and Twitter as representative media for investigating information supply and demand, respectively. We present the notion of News Value Index (NVI), which evaluates the value of news information in terms of the magnitude of Twitter messages associated with it. In addition, we visualize the change of information value over time using the NVI. We conducted an analysis using 387,014 news articles and 31,674,795 Twitter messages. The analysis results revealed interesting patterns: most issues show lower NVI than average of the whole issue, whereas a few issues show steadily higher NVI than the average.