• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.113-122
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• 2011

Social Network Service(SNS) has become a hot trend as a web service which helps users construct social relationships in the web and enables online communication. The information about user activities and behaviors obtained from the SNSs is expected to be an useful knowledge source for other services such as recommendation services. Most of previous researches on SNS rely on analyzing overall network topology and surveying the activities in a one-dimensional aspect. This paper propose a system for measuring multi-dimensional interaction through the activities in a SNS. The proposed system delivers an unified profile (consisting of profile and multi-dimension interaction) model from user-activities in Twitter.com. At the experimental section, some meaningful perspectives on a set of the unified profiles are described.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.207-226
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• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.252-255
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• 2011

Growing demand for smart phones and data communication diminishes the constraints of Twitter and Facebook than a smartphone has become a subject of interest. On the other hand facebook users in their relationships to obtain the consent of the other, twitter is a relatively simple procedure for the information ripple effect is excellent. Twitter is beyond a simple social networking services(SNS) located in one of the popular media and powerful have the upper retweet. Retweet to the top of his sympathy with the ability th send tweets to their subscriber information can spread quickly. In this paper, we propose the smart retweet that system actively extend the existing retweet. In order to realize the smart retweet and additional criteria for determining the destination of the information is required. Based on tweet generated regional or an local information mentioned to tweet, to determine the destination. Smart retweet of the speed and scope of information transmission through the scale is expected.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.341-348
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• 2014

The advance in web accessibility with dissemination of smart phones gives rise to rapid increment of users on social network platforms. Many research projects are in progress to detect events using Twitter because it has a powerful influence on the dissemination of information with its open networks, and it is the representative service which generates more than 500 million Tweets a day in average; however, existing studies to detect events has been used TFIDF algorithm without any consideration of the various conditions of tweets. In addition, some of them detected predefined events. In this paper, we propose the RTFIDF VT algorithm which is a modified algorithm of TFIDF by reflecting features of Twitter. We also verified the optimal section of TF and DF for detecting events through the experiment. Finally, we suggest a system that extracts result-sets of places and related keywords at the given specific time using the RTFIDF VT algorithm and validated section of TF and DF.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.69-74
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• 2012

Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2878-2884
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• 2015

Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users' exact location. Then, we propose a new tweet bot detection algorithm by using both an entropy based on geographic variable of each user and device information of each user. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.4
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• pp.75-90
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• 2013

Given the context that users are actively using social media with multimedia embedded information, the purpose of this study is to demonstrate how images are used within Twitter messages, especially in influential and favorited messages. In order to achieve the purpose of this study, the top 200 influential and favorited messages with images were selected out of 1,589 tweets related to "Boston bombing" in April 2013. The characteristics of the message, image use, and user are analyzed and compared. Two phases of the analysis were conducted on three data sets containing the top 200 influential messages, top 200 favorited messages, and general messages. In the first phase, coding schemes have been developed for conducting three categorical analyses: (1) categorization of tweets, (2) categorization of image use, and (3) categorization of users. The three data sets were then coded using the coding schemes. In the second phase, comparison analyses were conducted among influential, favorited, and general tweets in terms of tweet type, image use, and user. While messages expressing opinion were found to be most favorited, the messages that shared information were recognized as most influential to users. On the other hand, as only four image uses - information dissemination, illustration, emotive/persuasive, and information processing - were found in this data set, the primary image use is likely to be data-driven rather than object-driven. From the perspective of users, the user types such as government, celebrity, and photo-sharing sites were found to be favorited and influential. An improved understanding of how users' image needs, in the context of social media, contribute to the body of knowledge of image needs. This study will also provide valuable insight into practical designs and implications of image retrieval systems or services.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.89-107
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• 2021

This paper explores the empirically analyzes the competitive nature of the five social media by analyzing the proper SNS service such as Facebook, Instagram, YouTube, Pinterest, and Twitter. In this study, we surveyed the use and satisfaction of social media for SNS users by using the proper theory. A total of 224 users were selected for analysis. Based on the results of the questionnaire, factor analysis was carried out to extract common factors such as relationship, sociality, convenience, daily life, and entertainment. As a result of the research using proper analysis, Facebook showed the widest narrowness in sociality (.627) and convenience (.636) in the first place, and YouTube showed the lowest in daily life (.670) and entertainment (.615) In the relationship (.520), the Instagram was the widest. In terms of five factors, Facebook and YouTube have the greatest overlap in relationship (1.826) and sociality (2.696), while Pinterest and Twitter are the most common in daily life (1.937) and entertainment (2.263) There is redundancy, and for convenience (2.583), YouTube and Twitter have the most redundancy. Facebook, Instagram, and YouTube have a competitive advantage over Pinterest in terms of relationships, sociality, convenience, routine, and entertainment, and are competitive across all factors except Facebook, Instagram, and YouTube Twitter It is possible to confirm that it is superior.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.309-316
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• 2016

While consumption patterns have changed online music, online music platform began to emerge. While people prefer popular music recommendation, they use the online music platform chart or use the SNS Platform to share information. Online platform Ranking is different because of different properties held by members. Meanwhile, we need music charts characteristics of SNS users. So there were a lot of attempts to chart a comprehensive variety of platforms. And continue to emerge theses linking the musical characteristics and SNS. In this paper, We have developed a new chart using the behavior of Twitter Users who listen to music, and studies comparing the results with existing chart.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.419-428
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• 2020

SNS media is gaining its media share with the benefits of digital technology, such as the convenience of physical access and the entertainment and interactivity of contents, and are becoming a part of users' lives. As media contents consumers move from traditional media to SNS, marketing communication activities are rapidly adapting to leading SNS platforms such as Facebook. This study compares how users perceive four advertisement attributes in each SNS, focusing on Facebook, Instagram, Kakao Story, and Twitter, where the media content creation and consumption systems are relatively similar to each other. The impact on eWOM intention was identified. In addition, we discussed effective SNS operation.