• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.345-350
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• 2016

This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.

• Journal of Distribution Science
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• v.14 no.5
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• pp.97-106
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• 2016

Purpose - Social commerce is a certain way of how people buy some products together with others through the internet sites with mutual interactions among customers with the benefits of SNS when buying some products. At present, China market has some problems due to its rapid growing. However, empirical research or academic approach to social commerce has not been made enough. So, it is important for Chinese social market to develop and enlarge the customers with stability under the reliability and satisfaction. Also it is important for them to have repurchase intention. Nowadays, it is necessary to find the factors on customer satisfaction and trust, whereas consumers' dissatisfaction and unreliability are increasing on social commerce recently. In addition, researches on social commerce have been actively pursued by a variety of domestic and foreign scholars. However, researches on social commerce and Chinese market are short of, and they have some limitations because of the rapid growth of the market even though it is the early stage. The current situation requires researches on consumers' repurchase intention for continuing growth in the future according to the growth of Chinese social commerce. Research design, data, and methodology - The literature and the empirical studies are combined in order to achieve the purpose of the study. Deriving social commerce features and consumer properties as factors affecting the repurchase intention through the literature, and these factors have modeled a series of assumptions about the impact on satisfaction and trust, and have established hypotheses to verify them. The survey which is conducted to test the hypothesis and questionnaires are derived based on the variables discussed in the previous study. Appropriate measures were developed and tested on 227 respondents in China with a cross-sectional questionnaire survey. The path relationships of the research model were analyzed by SPSS 23.0 and Amos 23.0. Results - Research results about social commerce characteristics and factors affecting the repurchase intention are presented to Chinese market companies that adopt business models and consumer characteristics. In addition, this study focuses on the characteristics of social commerce, from two-dimensional characteristics of the consumer satisfaction, trust and the impact on the repurchase. Therefore, social commerce features and consumer properties based on the results of this study may lead the strategic implications that may increase the repurchase intention. Conclusions - The classification reviewing the previous findings related to social commerce and social commerce features affects social commerce repurchase (price discount, interactivity) and consumer characteristics (impulsivity, innovation, collectivism). It affects repurchase on factors and analyzes empirically. The empirical results identify major characteristics (social commerce characteristics, attributes) that affect the repurchase intention, and give the practical implications as well as the business strategies that are able to enhance social commerce repurchase consumers. Social commerce is a certain way of how people buy some products together with others through the internet sites with mutual interactions among customers with the benefits of SNS when buying some products.

• Asia pacific journal of information systems
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• v.31 no.2
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• pp.197-219
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• 2021

Social media platforms have become prominent channels for e-commerce, and the role of social network sites' (SNS) content marketing is expanding as a strategic marketing communication approach to attract and retain consumers and increase sales. In this study, we focused on South Korea market and explored the influence of linguistic complexity and informality on consumer engagement. In particular, we identified the importance of complexity, focusing on its negative effects, as well as the moderating effect of commerce features to minimize these effects. Specifically, content length, hashtags, long words, and average sentence length significantly and negatively impacted consumer engagement. The influence of emojis, an informality variable, was not statistically significant. Shoppable tags, a commerce feature that provides both advertising explicitness and shopping convenience, were a moderating factor in the influence of complexity. Our findings provide new insights for content marketing researchers, and have practical implications for social media managers and content developers.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.571-580
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• 2017

Recently, there are many communication softwares based on text on various smart devices. Unlike traditional print publishing, mobile publishing and SNS tools tends to utilize more decorative or more emotional fonts so that users can pass some feelings from contents. So font providers have released new fonts which deal with the requirements of the market. Nevertheless being released lots of new fonts, general users have not used them because they searched only by font name or font provider's name. It means that there is no way for users to know and find new things. In this study, we suggest font shape classification rules for font registration system based on font design features. We proved the validity of classification standard study through some experiments with 50 commercial fonts. Also the result of this study was provided for Korea Telecommunication Technology Association and adopted by the Korea industrial standard.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.700-709
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• 2011

In this paper, we propose a novel method which can detect ordnance firing events in IR images. The proposed algorithm is comprised of effective target detection and robust clutter rejection methods based on the temporalspatial cooperative filter. And additional clutter reduction is performed based on the proposed two features, NTFF (Number of Temporal Filter Frames) and SNS(Size-Normalized Signal). Experimental results show the effectiveness and feasibilities of our proposed algorithm.

• International Commerce and Information Review
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• v.18 no.2
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• pp.3-26
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• 2016

Crowdfunding is growing up SNS spread, raising funds from numerous people which are offering necessary funding of private and enterprise using the internet-based platforms. The function of crowdfunding uses innovative financing in being business and start-ups having difficulty in financing. Crowdfunding in our domestic is concentrated on culture and arts in the early adoption phase, and is numerous projects with public features. However, the investment case of crowdfundig in e-commerce trade start-ups is rarely in spite of a increased attention for crowdfunding.. The purpose of this study examined the factors to be considered when using successfully crowdfunding on e-commerce trade start-ups. This study is understanding the legal and policy of crowdfunding market status and other countries, and suggests the activation of the direction of government policy, legal system, and participation of financing suppliers and investors as activating the law and policy system related the crowdfunding on a domestic in terms of e-commerce trade start-ups investment.

• Fashion & Textile Research Journal
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• v.22 no.1
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• pp.102-111
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• 2020

This research examined the effects of Narcissism as socio-psychological factor and physical features such as BMI on the selfie behavior. The participants of the study were SNS users aged 20-29 years recruited in Daegu and surrounding areas. Statistical analysis including factor analysis, ANOVA, cluster analysis and regression was done using SPSS 23.0 to analyze the results. Two groups with high other-dependent Narcissism showed higher levels of Cosmetic surgery management and clothing management compared to other groups. Among 4 clusters divided by BMI, the lower the BMI, the higher the level of appearance management behavior except for body management. Other-dependent Narcissism and BMI have been shown to have a significant effect on selfie behavior in the relationship of Narcissism, BMI and selfie behavior. In the relationship between appearance management behavior and selfie behavior, only beauty treatment management influenced selfie improvement behavior, and all appearance management behaviors affected selfie complement behavior. Fashion brands should use the relationship between Narcissism, appearance management behavior and selfie behavior in planning selfie related events on SNS as well as consider active collaboration with cosmetics brands. It is necessary to investigate selfie behavior from a more diverse perspective by expanding future research targets and diversify related variables.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1069-1086
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• 2013

Nowadays, web defacement becomes the utmost threat which can harm the target organization's image and reputation. These defacement activities reflect the hacker's political motivation or his tendency. Therefore, the analysis of the hacker's activities can give the decisive clue to pursue criminals. A specific message or photo or music on the defaced web site and the outcome of analysis will be supplying some decisive clues to track down criminals. The encoding method or used fonts of the remained hacker's messages, and hacker's SNS ID such as Twitter or Facebook ID also can help for tracking hackers information. In this paper, we implemented the web defacement analysis system by applying CBR algorithm. The implemented system extracts the features from the web defacement cases on zone-h.org. This paper will be useful to understand the hacker's purpose and to plan countermeasures as a IDSS(Investigation Detection Support System).

• Journal of Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.