• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.141-148
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• 2011

Recently, various social network services have been grown. Among them, personal relationships based social network services such as Facebook and Twitter make a remarkable growth of industry. In such services, users' profiles are very important for establishing the relationship between two users. However some information in a user's profile causes the leakage of the user's privacy, and thus we have to deal with the information in the profile. Especially, we have to treat contact information, such as the phone number and the e-mail address, very carefully since an adversary can use the information to violate the user's privacy in real life. In this paper, we propose two profile management systems that can enhance the privacy of users in social network services. We compare our systems with existing profile management techniques in well-known social network services such as Facebook and Twitter, and show that our systems provide enhanced privacy.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.640-645
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• 2014

Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.1
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• pp.167-194
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• 2019

This study aims to examine the change of awareness on presidential archives and records before and after impeachment by analyzing user frames. To achieve the goal of this study, prior studies of frame analysis were reviewed, and tweets of presidential archives and records before and after the impeachment were collected. This study conducted an analysis of Twitter and news extracted from Twitter using user frames and determined the differences between each frame over time. Afterward, five frames were set up to be used for the research through prior research and Twitter network analysis; changes in frames over time were examined by analyzing Twitter and news extracted from Twitter. Through such frame analysis, changes in the frame of presidential archives and records before and after the impeachment were examined, changes in public perception of presidential archives and records were identified, and areas of interest were determined. This study is significant as it identified changes in the public perception of presidential archives and records as well as in the areas of interest for the general public.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.177-188
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• 2013

As the number of smart phone users increases, many organizations begin to adopt social media rapidly to diversify communication channels with customers. Specifically, micro-blog, which supports instant and two-way communications between users and between organizations and users, has been adopted by many organizations as an efficient way not only to identify new customers but also to retain existing customers. The purpose of this study is to investigate the usage motivation and satisfaction of enterprise twitter based on use and gratifications perspectives comparing with Korea and China. Based on prior studies on use and gratifications of internet-related media, information seeking, pleasure/entertainment, relationship, communication, and incentives were identified as usage motivations of enterprise Micro-blog. This study contributes to provide the base of activation strategies and practical implications for micro-blog as a marketing tool.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.1-10
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• 2012

Recently, Social Network services(SNS) is gaining popularity as Facebook and Twitter. Popularity of SNS leads to active service and social data is to be increased. Thus, social search is remarkable that provide more meaningful information to users. but previous studies using social network structure, network distance is calculated using only familiarity. It is familiar as distance on network, has been demonstrated through several experiments. If taking advantage of social context data that users are using SNS to produce, then familiarity will be helpful to evaluate further. In this paper, reflect user's attention through comments and tags, Facebook context is determined using familiarity between friends in SNS. Facebook context is advantageous finding a friend who has a similar propensity users in context of profiles and interests. As a result, we provide a blog post that interest with a close friend. We also assist in the retrieval facilities using Near Field Communication(NFC) technology. By the experiment, we show the proposed soicial search method is more effective than only tag.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.91-113
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• 2024

In the wake of the COVID-19 pandemic, unsubstantiated information concerning vaccines and the coronavirus has proliferated on various social media platforms. Consequently, we have considered viable actions to mitigate the impact of such unverified content, enabling individuals to use social media platforms more effectively and minimize any ensuing confusion. Recent measures in this area have included YouTube's practice of labeling vaccine or corona videos as authoritative when emanating from reputable organizations and Twitter's practice of flagging vaccine-related content as potentially misleading or taken out of context. This study seeks to explore how such contrasting labeling practices influence users' believability and engagement differentially, while also examining the moderating impact of regulatory focus. The results indicate that authoritative labeling positively influenced users' believability and engagement, whereas misleading labeling adversely affected users' believability and engagement. Additionally, our findings revealed that authoritative labeling has a stronger impact on promotion-focused individuals, while misleading labeling has a more pronounced effect on prevention-focused individuals. Our findings offer insights into how social media platforms can design and present information to their users, taking into account their regulatory focus.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1636-1637
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• 2015

소셜 네트워크 서비스(SNS)에서는 사용자들의 친한 관계를 나타내는 여러 가지 특징을 발견할 수 있다. 본 논문에서는 트위터 이고-네트워크(ego-network) 데이터를 이용한 분석 실험을 통해 유저 간 친밀한 정도를 나타내는 여러 특징들과 관심사 유사도의 상관관계를 밝힌다.

• Korean journal of communication and information
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• v.66
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• pp.59-86
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• 2014

This study explored how rumor is diffused via Twitter and how the characteristics of rumor affect the interactivity among users in the Korea-U.S. FTA case. A key word search located three issues as major ones related to the Korea-U.S. FTA: appendectomy myth, collapse of health insurance, and increases in medicine prices. The arousal of rumor has two dimensions: fact and expression. The fact arousal was the highest in the issue of 'appendectomy myth', and the expression arousal the highest in 'increases in medicine prices'. The rumor diffusion took the 'explosive wave' in the issue of appendectomy myth, the 'latent wave' in the issue of increase in medicine prices, and the 'repetitive wave' in the issue of collapse of health insurance. Correlation analyses revealed a high correlation between the arousal intensity of rumor and the user interactivity in the issue of collapse of health insurance. The study showed that Twitter took a role of diffusing negative messages about the Korea-U.S. FTA. Results implies that government officials and journalists pay attention to Twitter for sensing the public opinion when building policies and managing crises.