• Journal of Digital Convergence
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• v.16 no.12
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• pp.407-415
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• 2018

The purpose of this study is to investigate the effect of SNS addiction tendency and social support on cyber violence among college students. Data collection was conducted through a structured questionnaire for 330 college students and analyzed through SPSS 18.0 program. As a result of the analysis, SNS addiction tendency was low as $1.81{\pm}0.55$, social support was $4.00{\pm}0.78$ and cyber violence was $1.38{\pm}0.59$. There was a significant positive correlation between SNS addiction tendency and SNS posting frequency, and there was a significant negative correlation between social support and grade. Cyber violence increased as the tendency of SNS addiction increased, as social support decreased, and cyber violence decreased in the second and third graders compared to the first grade. For this purpose, it is necessary to strengthen cyber education and group counseling program suitable for college students and it will be necessary to make effort to raise self - control.

• Smart Media Journal
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• v.5 no.3
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• pp.54-61
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• 2016

According to many smart devices are development, SNS(Social Network Service) users are getting higher that is possible for real-time communicating, information sharing without limitations in distance and space. Nowadays, SNS users that based on communication and relationships, are getting uses SNS for information sharing. In this paper, we used the SNS posts for users to extract the category and information provider, how to following of recommend method. Particularly, this paper focuses on classifying the words in the text of the posts and measures the frequency using Inception-v3 model, which is one of the machine learning technique -CNN(Convolutional Neural Network) we classified image word. By classifying the category of a word in a text and image, that based on DMOZ to build the information provider DB. Comparing user categories classified in categories and posts from information provider DB. If the category is matched by measuring the degree of similarity to the information providers is classified in the category, we suggest that how to recommend method of the most similar information providers account.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.397-400
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• 2022

본 연구에서는 소셜 네트워크 서비스(Social Network Service, SNS) 데이터로부터 약물 부작용 게시글을 추출하기 위한 순환 신경망(Recurrent Neural Network, RNN) 기반 분류 모델을 제안한다. 먼저, 처방 빈도가 높으며 게시글을 많이 확보할 수 있는 케토프로펜 약물에 대하여 국내 최대 소셜 네트워크 플랫폼인 네이버 블로그와 카페의 게시글(2005 년~2020 년)을 확보하고 최종 3,828 건을 분석하였다. 결과적으로 케토프로펜에 대한 3 종(약물, 부작용, 불용어)의 렉시콘을 정의하였으며 이를 기반으로 Bi-LSTM 분류모델 기준 87%의 정확도를 얻었다. 본 연구에서 제안하는 모델은 SNS 데이터가 약물 부작용 정보 획득을 위한 기존 (전자의무기록, 자발적 약물 부작용 보고 시스템 등) 자료원에 대한 보완적 정보원이 되며, 개발된 Bi-LSTM 분류모델을 통해 약물 부작용 게시글 추출의 편리성을 제공할 것으로 기대된다.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.17-24
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• 2018

One of the major issues in big data processing is extracting keywords from internet and using them to process the necessary information. Most of the proposed keyword extraction algorithms extract keywords using search function of a large portal site. In addition, these methods extract keywords based on already posted or created documents or fixed contents. In this paper, we propose a KAES(Keyword Advertisement Extraction System) system that helps the potential shopping keyword marketing to extract issue keywords and related keywords based on dynamic instant messages such as various issues, interests, comments posted on SNS. The KAES system makes a list of specific accounts to extract keywords and related keywords that have most frequency in the SNS.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.812-815
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• 2017

소셜 미디어를 이용하는 사용자의 수가 증가함에 따라 소셜 미디어에서 공유되고 있는 유해 정보(불법, 음란)의 심각성의 대두되고 있다. 기존의 단어 DB기반의 유해 사이트 판별 방법은 단어 DB의 갱신 문제점과 유해 정보와 낮은 연관성을 가진 단어가 DB에 저장되는 문제점을 가지고 있었다. 또한 링크 주소를 짧게 해주는 Short URL 서비스를 고려하지 않아 잘못된 웹 문서를 판별 대상으로 삼을 수 있는 문제점이 있다. 본 논문에서 제안하는 유해 사이트 판별 방법은 기 구축한 유해 단어 DB에서 유해 단어를 추출하고, 추출된 단어를 포함하는 소셜 미디어상의 유해 게시물을 조회한다. 유해 단어 DB를 구축하는 방법으로, 유해 게시물 조회시 내용에 포함되는 해시태그를 저장하는 방법을 사용하여 게시물 수집과 동시에 유해 단어 DB를 갱신시킨다. 또한 유해 게시물 내용에 있는 URL 링크의 웹 문서를 문자열로 치환하여, 해당 문자열내의 유해 단어 DB에 있는 유해 단어의 등장 빈도 수를 계산하고 이를 기준치와 비교하여 유해도를 판단한다. Short URL을 사용한 URL 링크인 경우 HTTP 응답 메시지의 헤더 부에 존재하는 실제 목적지 URL 주소를 가져와 유해도 검사를 실시한다.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.73-85
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• 2012

Recently, as Social Network Services(SNS), such as Twitter, Facebook, are becoming more popular, much research has been doing actively. However, since SNS has been launched recently, related researches are also infant level. Especially, search engines serviced in web potals simply show the postings in order of upload time. Searching the postings in Twitter should be different from web search, which is based on traditional TF-IDF. In this paper, we present the new method of searching and ranking the interesting postings in Twitter. In proposed method, we utilize the frequency of retweets as a major factor for estimating the quality of postings. It can be an important criteria since users tend to retweet the valuable postings. Experimental results show that proposed method can be applied successfully in Twitter search system.

• Smart Media Journal
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• v.5 no.1
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• pp.61-68
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• 2016

According to increasing SNS users and developing smart devices like smart phone and tablet PC recently, many techniques to classify user emotions with social network information are researching briskly. The use emotion classification stands for distinguishing its emotion with text and images listed on his/her SNS. This paper suggests a method to classify user emotions through sampling a value of a representative figure on a trigonometrical function, a representative adjective on text, and a canny algorithm on images. The sampling representative adjective on text is selected as one of high frequency in the samplings and measured values of positive-negative by SentiWordNet. Figures sampled on images are selected as the representative in figures; triangle, quadrangle, and circle as well as classified user emotions by measuring pleasure-unpleased values as a type of figures and inclines. Finally, this is re-defined as x-y graph that represents pleasure-unpleased and positive-negative values with wheel of emotions by Plutchik. Also, we are anticipating for applying user-customized service through classifying user emotions on wheel of emotions by Plutchik that is redefined the representative adjectives and figures.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.245-251
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• 2022

This study attempted to examine the effect of virtual influencer, which is highly interested in consumers, and has recently been increasing the frequency of use in corporate marketing activities. In particular, the purpose is to examine the effect of the attributes of virtual influencer on consumers as an information source for a product or brand. To this end, the effect of perceived attractiveness, trustworthiness, and expertise of virtual influencer on SNS usage intention and imitation intention consumers was examined. As a result of the study, it was found that attractiveness among the attributes of virtual influencer had a positive effect on the usage intention, and attractiveness and trustworthiness had a positive effect on imitation intention. In addition, it was found that imitation intention had a positive effect on usage intention. In other words, the perceived attractiveness of virtual influencer by consumers through SNS is the most important attribute. These findings imply that when virtual influencer is used in marketing, companies must perceive the attractiveness of virtual influencer through content posted on SNS as well as their attributes as information sources. We expect the results of this study to provide major implications for marketing activities such as companies and public institutions that consider using virtual influencer.