• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.73-85
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• 2014

This study analyzed the profile of a Facebook account to examine how personal information is revealed and what kinds of factors influence personal information revelation. Categories of user's profile on Facebook were analyzed and two dimensions were developed; the degree that how much personal information is revealed and the network limits that personal information is accessed. Main variables to determine personal information revelation are Facebook privacy concern and uses for social relationships along with gender, the duration of Facebook use, and average time of use. Data were collected from college students. Factor analysis produced two factors of Facebook privacy concern, Facebook privacy concern with users and Facebook privacy concern with the Facebook system. Regression analyses were performed to identify significant determinants of the degree of information revelation and the network limits of personal information. The results found out that the degree of personal information revelation is explained by gender, the duration of use, and use for social relationships while the network limit is explained by the duration of use and Facebook privacy concern with users. Worthy of notice is that use for social relationships and Facebook privacy concern with the Facebook system offset each other. The implications of the results are discussed. Additionally and finally the categories of profiles are graphically re-grouped to show how personal information revelation is associated with social relationship generation and maintenance.

• Journal of Smart Tourism
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• v.1 no.3
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• pp.31-44
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• 2021

The emergence of the digital economy and easy accessibility to Web 2.0 tools has seen an expansion of the influencer ecosystem within the travel and tourism industry. Founded on the principles of reference groups and peer reference there is a growing trend amongst industry practitioners who are now opting to move away from many of the traditional approaches used to market their products and services and are instead taking advantage of the concept of e-word-of-mouth (eWOM). Whilst there is a growing body of academic literature addressing the notion of influencer marketing, there is little understanding of influencer marketers themselves. Consequentially, this study addresses this gap in the literature through the quantitative examination of those who promote products, services, or companies by distributing eWOM through their online digital channels and presence; otherwise known as travel influencers. A quantitative research approach involving an online survey yielded 255 responses from travel influencers. The research findings indicate that those who work in this field prefer not to be awarded the label "travel influencer," focusing instead on their specific method of influencing, such as blogging and vlogging or sharing Instagram updates. The research also demonstrates how the new influencers have a strong role in generating travel urge and desire. The research contributes to the wider body of academic literature and travel industry practitioners by establishing the general profile of influencers and their increasingly specialized role in tourism and hospitality marketing.

• Korean Journal of Childcare and Education
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• v.19 no.2
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• pp.43-59
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• 2023

Objective: This study investigated the differences in preschool children's perceptions of artificial intelligence (AI) and their distribution by latent profiles according to their experience with AI robots in daycare centers. Methods: The participants included 119 five-year-old children, 52 of whom had experience with AI robots in daycare centers and 67 of whom did not. Children's perceptions of AI were measured using the Godspeed scale from Bartneck et al.(2009). Data were analyzed using a t-test, latent profile analysis, and chi-square test. Results: The results showed that compared to the inexperienced group, the experienced group reported lower levels of animacy and perceived intelligence of AI robots, indicating higher levels of AI knowledge and understanding. In addition, the experienced group had a higher probability of belonging to the 'machine recognition' type than 'organism recognition' type, although the difference was not statistically significant. Conclusion/Implications: The findings suggest that experience with AI robots in daycare centers can improve children's AI knowledge and understanding. To further enhance this effect, it is necessary to increase the number of robots put into classrooms, and to consider various teaching media that reflect children's preferences.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.339-345
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• 2023

Design exists behind technology. Design is intertwined with the needs of daily life and market structures, and while dealing with technology, it can become insensitive to its meaning. Unlike other social media platforms, Instagram consists of image-based content. The purpose of this study is to examine the addictive design of Instagram. Furthermore, we discuss the ethical responsibilities that designers must have. A theoretical framework for understanding Instagram design is established through a review of major domestic and international literature that has been previously studied. Understand the history, structure, and functions of Instagram and identify Instagram designs that promote social media addiction. In this study, we introduced the mechanism by which Instagram promotes user addiction through design issues. (1) Pull-to-Refresh (2) Red color in push alarm (3) Profile photo border expression in Instagram Story. This design stimulates users' social desires and FOMO, forming the structure of obsessive Instagram usage habits. Instagram is an example that forces us to reconsider the ethical role of design and designers along with the advancement of technology. In today's world, the intrinsic value of what they create, including our society and life itself.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.159-168
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• 2011

As e-commerce and social media service evolves, studies on recommender systems advance, especially concerning the application of collective intelligence to personalized custom service. With the development of smartphones and mobile environment, studies on customized service are accelerated despite physical limitations of mobile devices. A typical example is combined with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A profile of movie genre similarity is generated and designed to provide related service in mobile experimental environment before prototyping and testing with data from MovieLens.

• Journal of Information Science Theory and Practice
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• v.8 no.1
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• pp.68-78
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• 2020

This study attempted to uncover the characteristics of health information non-seekers among the United States foreign-born population and identify potential predictors of their non-seeking behavior. The trends of foreign-born health information nonseekers over the past twelve years were also examined. Statistical analysis was conducted with two sets of Health Information National Trends Survey (HINTS) data: HINTS 2 (2005) and HINTS 5 Cycle 1 (2017) datasets. It was found that foreign-born nonseekers differ from foreign-born seekers in terms of a variety of variables including education, income, English proficiency, the uses of Internet and social media, ownership of digital devices, ownership of health insurance, perceived health status, and level of trust in health information sources. Among them, education, Internet use, and trust in online health information were identified as predictors of the foreign-born population's non-seeking of health information. In addition, three variables - race/ethnicity, age, and place of accessing the Internet - which were significant factors in the 2005 dataset, were no longer significant in the 2017 dataset, implying the possible influence of smartphones that reduces Internet accessibility gaps among different racial/ethnic and age groups.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.199-201
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• 2023

국내·외 소셜미디어 사용자가 증가하면서 마약 유통, 불법 촬영물 유포 등과 같은 사이버 범죄가 소셜미디어를 통하여 발생하고 있다. 사이버 범죄 수사를 위해 소셜미디어 크롤링 연구가 진행되고 있지만, 주로 'N번방' 등 불법 촬영물 및 성 착취물 유포와 같은 성범죄 수사를 대상으로 한다. 그러나 최근에는 성범죄뿐 아니라, 소셜미디어를 통한 마약 유통이 급격히 증가하고 있으므로 소셜미디어 크롤링을 통한 마약 수사 기술 개발이 필요하다. 본 논문에서는 소셜미디어 중, 인스타그램의 마약 유통을 추적하기 위해 실제 사용되는 마약 은어를 정의하고, 정의된 은어를 검색 키워드로 입력하여 사용자 계정을 수집하였다. 또한, 수집된 사용자 계정의 프로필 사진을 추출하고 유사도 분석을 수행하여 동일 마약 유통자 식별에 프로필 사진이 효과적임을 검증하였다.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.150-156
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• 2024

With the advent of personalized search engines, a myriad of approaches came into practice. With social media emergence the personalization was extended to different level. The main reason for this preference of personalized engine over traditional search was need of accurate and precise results. Due to paucity of time and patience users didn't want to surf several pages to find the result that suits them most. Personalized search engines could solve this problem effectively by understanding user through profiles and histories and thus diminishing uncertainty and ambiguity. But since several layers of personalization were added to basic search, the response time and resource requirement (for profile storage) increased manifold. So it's time to focus on optimizing the layered architectures of personalization. The paper presents a layout of the multi agent based personalized search engine that works on histories and profiles. Further to store the huge amount of data, distributed database is used at its core, so high availability, scaling, and geographic distribution are built in and easy to use. Initially results are retrieved using traditional search engine, after applying layer of personalization the results are provided to user. MongoDB is used to store profiles in flexible form thus improving the performance of the engine. Further Weighted Sum model is used to rank the pages in personalization layer.