• 한국노년학
• /
• v.41 no.4
• /
• pp.505-525
• /
• 2021

Purpose. The purpose of this study is to examine how the phrase "old person" are expressed and used in the online sphere. Based on the theoretical concept of stigma, this study investigates the images and attitudes in society toward the elderly, and the characteristics of hate speech aimed at the elderly. Method. This study conducted text mining based on social big data using anonymous conversations. Results. It was confirmed that the elderly images shared online were generally negative. The attitudes expressed toward them also tended to be negative due to the negative images that are propagated of the elderly. The hate speech relating to the elderly, in usages such as 'Teul-ttag' and 'Kon-dae', were mainly identified in comments that negatively evaluate the elderly, and these expressions demonstrate the depth of hate and discrimination towards the elderly who are considered burdensome by young people. Interestingly, the hateful expressions towards the elderly were found more with regard to issues related to politics and economics and not just any content about the elderly. Conclusions. This study discussed the ways and means to enhance inter-generational understanding and solidity.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.219-240
• /
• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Korean Journal of Culture and Social Issue
• /
• v.19 no.2
• /
• pp.259-283
• /
• 2013

It seems that Internet based communication has been settled down in everyday life. Internet based communication studies also have been done and they proposed that internet based communication modal differs from other communications modal. One of the major themes about internet based communication was the effect of internet based communication on relationships. Early studies suggested that internet has negative effect on life and relationships, although it has positive effect on economics and information distribution. Because there is relative anonymity, People and Researchers thought that people easily could be exposed to negative situations like pornography, instant relationship, negative reply and soon. However,Recently there have been on going un-solving arguments about effect of internet based communication.From the negative perspective, Internet based communication is negative to relationship, because internet based communication could displace face to fact communication and old off-line relationships. However, from the positive perspective, researchers focused on the motivation and purpose of internet users. In this paradigm, people could expand their life and relationships using internet because internet could remove the various restrictions for relationship. Moreover they also suggested that people could enlarge their relationships because they could easily disclose theirselves in anonymity. However, No conclusion has been drawn yet and there needs some organization of two standpoints. Accordingly, This study is integrating the two perspectives and proposing future direction of internet based communication and relationship.

• Journal of Korean Society of Archives and Records Management
• /
• v.23 no.2
• /
• pp.1-25
• /
• 2023

This study aims to propose implications for the development of archival content of archives management institutions in Korea by analyzing cases of the archival content on Instagram of the national archives in the Anglosphere. The basic information of the research target's Instagram account, including the creation date, content, and the number of followers, was investigated, and the posts' contents and interaction types with high user responses were analyzed. As a result, to spread the records information service using Instagram, producing images and short-form content that can be intuitively checked through mobile screens and creating content that will attract the attention of primary users are required. Moreover, it is necessary to develop content for informative communications that can be shared with other users. There is also a need to enhance the exposure and searchability of the institution's Instagram account by strengthening connections with the institution's existing online resources and enabling communications, such as using hashtags, following related institutional accounts, and providing feedback on the contents' comments with followers. This study is meaningful in that it examined cases of archival content for Instagram and suggested their applications, and it can be used as basic data to help plan archival contents to spread the archival culture.

• Journal of the Korean Society for information Management
• /
• v.41 no.2
• /
• pp.97-130
• /
• 2024

The current debate in the academic community is on the criteria for predatory journals. Researchers are perplexed about what constitutes a predatory journal. The purpose of this study is to investigate how South Korean researchers discover and evaluate predatory journals. In order to achieve this, we collected 2,484 statements, comprising posts and comments, from Korean researcher communities, namely the Biological Research Information Center (BRIC), Hibrain.net, Phdkim.net, and the Scholarly Ecosystem Against Fake Publication Environment (SAFE). We divided the data into three primary categories-journals, publishers, and researchers-for the topic analysis. For each statement, we assigned 11 in-depth subtopic tags based on these categories. Six main points of contention emerged from the combinations of these sub-topic tags: (1) researchers' confusion about predatory journals and discussions about research performance; (2)(3) researchers' positive and negative perceptions of predatory journals; (4) researchers' evaluation criteria for journal quality and problems associated with the quality of Korean journals; (5) changes in publishing brought about by the introduction of open access (OA) and associated issues; and (6) discussions on broader issues within the academic ecosystem. By using a qualitative approach to examine how South Korean researchers view predatory journals, this study aims to advance basic knowledge of the discourse around them in the communities of domestic researchers.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.1-20
• /
• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.9 no.4
• /
• pp.1-12
• /
• 2014

It is essential factor for business operation to raise funds effectively. However, in Korea, many start-ups and small businesses have difficulties in fund-raising. In recent years, crowdfunding, a new method for funding a project of individuals or organizations by raising monetary contributions from a large number of people, has been growing up simultaneously with diffusion of social media. Crowdfunding is on early stage in Korea, and the majority of projects are focused on cultural or art categories. There is high proportion of projects that have social value in start-up sector. Crowdfunding in Korea has great potential because success rate of it is much higher than its of advanced countries, although market size is much smaller than them. The purpose of this paper is to propose success strategies of crowdfunding for start-up through case study. 5 crowdfunding platforms of Korea and Kickstarter, the platform of United States were investigated. Then we checked the figures related to the operation of the whole Korean projects on start-up. Finally, we made comparison between the cases of success and failure by analyzing 8 project characteristics. The study shows that it were the differences in trustworthiness and activeness of project creator, value of reward and efforts for interactivity that have great effects on success of the project. Whereas there was no significant influence of societal contribution and sponsor engagement. The thesis provides success strategies of crowdfunding for start-up as follows. Firstly, creator of the project should make support base by enthusiastic activites before launching funding project. Secondly, there should be contents that can easily show the process of business development in the project information. Thirdly, there must be appropriate design of rewards for each amounts of support money. Finally, efforts for interactivity, such as frequent updates, response for comments and SNS posting, should be followed after the launch of the project.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.49-67
• /
• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• The Journal of Society for e-Business Studies
• /
• v.17 no.4
• /
• pp.69-93
• /
• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).

• Journal of Internet Computing and Services
• /
• v.15 no.6
• /
• pp.133-144
• /
• 2014

As the Internet and smartphones prevail, this study investigated the purchasing factors of a new beauty marketing method, the social commerce, and verified the relationship of such purchasing factors to consumer satisfaction and repurchasing intentions. In order to verify the validity of purchasing factors, five sub-factors, such as service, price, interaction, convenience, and interest were classified, while consumer satisfaction and repurchasing intentions are grouped into one factor, using data about 20-39 years old. According to results of this study, purchasing factors in the beauty service markets through social commerce that had effects on the consumer satisfaction were price, service, convenience, and interest, but no relationship was found with interaction. We can predict that consumers buy not based on community activities among buyers or purchasing comments of others, but rather his/her own subjective thoughts and opinions about the services. As the result of repurchasing intention according to purchasing factors, affecting sub-factors were price, service, and convenience. Repurchasing intention is an positive response that reflects satisfactions. Since low price, satisfaction on the service, and convenience for busy modern people should be met, repurchasing intentions are not affected by interest, but rather systematic and professional service. Also, higher satisfaction on service raises repurchasing intention. In this study, it is clear that not only purchasing factors through social-commerce effect the satisfaction and the repurchasing intention, but also consumer satisfaction mediates partly purchasing factors and the repurchasing intention. And as sub-factors of purchasing factors, price, service, or convenience are more important to the consumer satisfaction than community or replies activities. Thus differentiated and professional customer service, the establishment and enhancement of trendy marketing should improve long term repurchasing intentions. This will lead to the increasing revenue of personal-shop and the developments of beauty markets, so strengthening product sourcing and promotion suitable for mobile shoppers are essential.