• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.105-117
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• 2021

This study examined the factors that influence the emotional expression in comments on the hot news about the 'Restaurant Collapse in Linfen City, Shanxi Province' published in Sina Weibo.. As a result of the study, first, there were differences in emotional expression according to gender. Women expressed stronger anger, disappointment, sadness, and condemnation than men. Second, the intensity of emotional expression of users in the eastern region was significantly higher than that of users in the central and western region. Third, the greater the number of Weibo, the total number of blogs where users participated in comments and posted emotional expressions, the stronger the emotional expression was. Fourth, unauthenticated users showed stronger emotional expressions of disappointment and sadness than authenticated users. The results of this study present implications for the factors influencing emotional expression on hot news. This study is meaningful in that it can be compared with social networks such as Twitter and Facebook in the West by looking at the factors that influence emotional expression in the process of online public opinion formation in China, and also meaningful in that a big data analysis method was used in online news analysis.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.137-147
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• 2014

The study aims at exploring motivation, rationale, and values in twitter users' retweet behavior. It proposes that diffusion of message is based on the complex interactional relationships among attributes of original message, user's rationale, and values. Based on a pilot study, we constructed a total of 34 questions asking message attributes, motivation, and values of retweeting. Then, twitter users participated in an online survey, in which they evaluate their own 5 retweet messages based on the constructed questions(5 messages ${\times}$ 34 questions = 170). Then, a factor analysis is done in order to see the dimensions of the concepts in retweet behavior; and understand how message attributes, motivations, and values are inter-related with each other. The main factors extracted were: (1) public fairness, (2) fun and playfulness, (3) communal help, (4) news and information, etc. Factor 2 and 4 show the traditional journalism characteristics; while factor 1 and 3 do alternative journalistic values. The latter may work as a rectifying factors for traditional journalism; however, backfiring mechanism for group polarization. In addition, (1) users' internal identities, (2) communal unity and (3) belongness were identified as rationales and values for retweet behavior.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.147-155
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• 2016

According to rapid development of technology, web text is growing explosively and attracting many fields as substitution for survey. The user of Facebook is reaching up to 113 million people per month, Twitter is used in various institution or company as a behavioral analysis tool. However, many research has focused on meaning of the text itself. And there is a lack of study for text's creation subject. Therefore, this research consists of sex/age text classification with by using 20,187 Facebook users' posts that reveal the sex and age of the writer. This research utilized Convolution Neural Networks, a type of deep learning algorithms which came into the spotlight as a recent image classifier in web text analyzing. The following result assured with 92% of accuracy for possibility as a text classifier. Also, this research was minimizing the Korean morpheme analysis and it was conducted using a Korean web text to Authorship Attribution. Based on these feature, this study can develop users' multiple capacity such as web text management information resource for worker, non-grammatical analyzing system for researchers. Thus, this study proposes a new method for web text analysis.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.158-166
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• 2013

This study gave attention to the people who has resistance of the social media that tens to be the use as innovations. Therefore, this study examined the determinant factors of psychological resistance of the social media such as Twitter focused on students as none-users of social media. Total of 268 none-users participated in this study. The results were as follows. First, relative advantage influenced negatively on innovation resistance. Second, perceived complexity influenced significantly not on innovation resistance. Third, perceived risk influenced positively on innovation resistance. Therefore, social media related to social relationship should improve relative advantage, the other way, decrease perceived risk such as defamation and personal attack.

• Knowledge Management Research
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• v.12 no.5
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• pp.25-40
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• 2011

Today, Internet users start off using heavily SNS(Social Network Site) such like, Facebook, Twitter. The reason of the growth of using SNS would be closely related to the various services of gaming, playing, entertainment items, sharing information etc., provided by the SNS, technically, the most important one out of the services provided would be behaving of sharing knowledge among people who connected and networked in the site. In sum, we assume that the users may communicate well each other and pay attention to build closely a social network using that kind of activities. However, nevertheless the new trends of communications and sharing knowledge become popular, researchers have just began the research issues in explaining why Internet user rush into SNS and enjoy the time in there. Therefore, we investigate on the reasons of posting knowledge voluntarily in the SNS and how others response to the posted information and actually affected by the behavior. We appled personal relation and social identity theory for this study, which personal relation in SNS may affect on social identity and make them produce knowledge generation. We found that social identity and involvement in SNS is closely related and influence knowledge creation and generation. This empirical study resulted in the importance of social relations in SNS, which leads to a sharing knowledge.

• Journal of Distribution Science
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• v.18 no.8
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• pp.23-33
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• 2020

Purpose: In the digital age, visual cues in computer-mediated environments are becoming a very popular means of communication. Therefore, it is a very critical market for marketers to utilize for marketing communication and platform providers and manufacturers of mobile devices which create and distribute the visual cues While the prevalent research on visual cue consumption focuses on the positive side, the dark side of consuming visual cues has not been investigated. Therefore, in this research, the dark side of using visual cues, such as difficulties and problems in their application, will be investigated. Research design, data, and methodology: Due to the nature of this study, a netnography approach was adopted. Twitter which the users regularly utilise visual cues in their communications was a prime source for data of this research. Results: This research suggests that visual cue users experience anxiety about the subordination of expression and suffer from the myth of an ideal practice of expression. Conclusions: As the previous research emphasised the complementary role of visual cues, has failed to recognise the problems associated with the extensive and growing dependence on visual cues. This awareness demonstrates that we need to take a careful approach to visual cue usage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2082-2102
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• 2018

In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users' preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available Last.fm dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Journal of Korean Society of Archives and Records Management
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• v.18 no.3
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• pp.165-191
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• 2018

The National Archives of Korea and other archives now provide services through social media such as Facebook or Twitter, and use such platforms to interact with users. To be specific, users communicate with and get information from these archives via the National Sinmoongo, the place for civil petition. Therefore, it is significant to understand the users' needs and the contents of the communication by analyzing the comments and petitions that have appeared in these channels. For this, this study analyzed users' perceptions and information needs shared through social media and the National Sinmoongo of the National Archives of Korea. The social media content analyzed here were posts and comments from the Facebook accounts of the National Archives of Korea, e-Record, the Busan Archives, and the Presidential Archives of Korea. Also, sentences containing the words "National Archives of Korea" and "Presidential Archives of Korea" that have appeared in texts in the National Sinmoongo were analyzed. Based on the analysis results, suggestions that could activate the user-centered archival information services in the archives and records centers were made.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.129-134
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• 2015

Since automatic social engineering based spam attacks induce for users to click or receive the short message service (SMS), e-mail, site address and make a relationship with an unknown friend, it is very easy for them to active in online social networks. The previous spam detection schemes only apply manual filtering of the system managers or labeling classifications regardless of the features of social networks. In this paper, we propose the spam detection metric after reflecting on a couple of features of social networks followed by analysis of real social network data set, Twitter spam. In addition, we provide the online social networks based unsupervised scheme for automated social engineering spam with self organizing map (SOM). Through the performance evaluation, we show the detection accuracy up to 90% and the possibility of real time training for the spam detection without the manager.