Earticle

Despite advances in Natural Language Processing (NLP) and Large Language Models (LLMs), developing a CBT chatbot that provides emotional support and fosters continuous engagement remains a challenging task. To address this, we proposed SupportlyChat, which is a fine-tuned LLaMA with a custom dataset and provides emotion analysis visualization. We curated the custom dataset by integrating mental health conversation dataset and HappyDB dataset which collects people’s happy moments. We conducted a user study with 10 participants using conditions including standard chatbot responses, emotion analysis reports with standard responses, and emotion analysis reports with empathetic responses. The findings indicate that participants found empathetic responses helpful in shifting towards positive thinking with our proposed method. Additionally, they preferred emotional visualization for its ability to enhance professionalism and recognize inherent emotions. Based on these findings, we suggest utilizing empathetic responses in psychological counseling to emotionally support clients and using emotion analysis reports to promote continuous engagement.

With the rise of short-form content and the growing integration of generative AI (Artificial Intelligence Generated Content, AIGC) technologies in drama production, this study extends the AI Device Use Acceptance (AIDUA) model to examine the factors influencing acceptance and rejection of AIGC dramas based on Chinese traditional mythology. Drawing on survey data from 528 Chinese viewers and employing structural equation modeling, the study identifies six key factors—social influence, hedonic motivation, perceived anthropomorphism, artistic quality, novelty, and cultural traditionality—as significant predictors of performance expectancy. Emotional responses, shaped by performance expectancy, were found to differentially influence acceptance and rejection intentions. The findings underscore the importance of emotional appraisal in user responses to AIGC content and provide empirical insights for the digital transformation of traditional intellectual property (IP) and the strategic planning of culturally resonant AIgenerated content.

The more precisely an AI system collects and analyzes user information, the more effectively it can tailor future recommendations for each user. However, gathering comprehensive information for individual users remains a significant challenge because they may have concerns about privacy or find the process bothersome. To encourage users to willingly provide diverse and meaningful personal information, we applied two widely discussed concepts in psychology and economics to conversational agents: Self-Disclosure and Utility Theory. Our study revealed that while both conversational strategies influenced user experience, the Utility Theory strategy, when combined with questions targeting opinions and emotions, enhanced users’ willingness to disclose personal information and improved their overall disclosure experience. These results highlight the importance of tailoring conversational strategies to information types to encourage self-disclosure effectively. Based on these findings, we propose design considerations for efficiently gathering user information through conversation.

We look at the business model development of a fan community platform ‘Weverse’ from two-sided platform (TSP) to multi-sided platform (MSP) and investigate its platform business model. From the Rocket Model’s theoretical perspective, the results reveal that Weverse firstly focuses on inviting artists as many as possible starting from BTS, then attracts new artists’ fans naturally. For success of this TSP, it forms MSP, ‘Weverse Shop’ to meet two sides’ relevant needs timely and filtered. In third stage of connection, various partnerships are attempted in terms of open platform strategies. For instance, by combining ‘VLive’ and Weverse, Naver’s fan platform business is transferred to Weverse. For core transaction through direct and indirect monetization, several cobranding activities are tried. Lastly, regarding optimization, newly born Weverse being launched in the first half of 2022 is supposed to create further synergies with Naver’s R&D capabilities in data, AI, and other technologies like metaverse platform ‘ZEPETO’ which already sells clothing items of Weverse artists.

We propose SQLGo, an interactive learning tool designed to help learne.rs intuitively understand the execution flow of SQL queries. SQLGo combines Common Table Expression based visualization techniques with a Large Language Model (LLM)-powered conversational interface to clearly convey intermediate operations and procedural semantics embedded in SQL.Most existing SQL learning tools provide only tabular query results, failing to adequately explain the intermediate states and execution process of a query. As a result, learners often struggle to grasp the actual execution flow underlying SQL’s declarative structure. SQLGo addresses these limitations by offering an interactive environment that supports procedural reasoning and encourages active learner engagement, thereby enabling more effective SQL learning.Through this system, learners are expected to intuitively grasp the execution flow of queries and gain a deeper and more meaningful learning experience compared to conventional tools.

Recently, the use of media has become a daily life due to Covid-19, and education using media has expanded for teenagers. The purpose of this study is to empirically verify how media usage characteristics affect social relationships and school adaptation through the self-efficiency and immersion of teenagers at the youth education site. This study was collected and distributed questionnaires to 250 high school students in Seoul from November 14 to November 21, 2020. The results of the study were derived with a total of 249 questionnaires, excluding one missing questionnaire, and the hypothesis was verified using a Covariance Structure Analysis. Research has shown that, first, the existing, individual, and utility among the characteristics of using youth media have a significant effect on self-efficiency, respectively. Second, only entertainment among the characteristics of youth media use was found to have a significant effect on immersion. Third, immersion has a significant definition effect on school adaptation, and self-efficiency has been shown to have a definition impact on social relationships. Based on this study, we believe that it will be used as a fundamental resource for the development of youth media usage and policy direction in the age of the ever-changing Covid-19.

Recently, there has been an increasing trend in the role of social media in tourism marketing. We analyze changes in tourism marketing trends using tourism marketing keywords through social media networks. The aim is to understand marketing trends based on the analyzed data and effectively create, maintain, and manage customers, as well as efficiently supply tourism products. Data was collected using web data from platforms such as Naver, Google, and Daum through TexTom. The data collection period was set for one year, from December 1, 2022, to December 1, 2023. The collected data, after undergoing refinement, was analyzed as keyword networks based on frequency analysis results. Network visualization and CONCOR analysis were conducted using the Ucinet program. The top words in frequency were 'tourists,' 'promotion,' 'travel,' and 'research.' Clusters were categorized into four: tourism field, tourism products, marketing, and motivation for visits. Through this, it was confirmed that tourism marketing is being conducted in various tourism sectors such as MICE, medical tourism, and conventions. Utilizing digital marketing via online platforms, tourism products are promoted to tourists, and unique tourism products are developed to increase city branding and tourism demand through integrated tourism content. We identify trends in tourism marketing, providing tourists with a positive image and contributing to the activation of local tourism.

This study aims to understand the determinants and effects of SNS photo sharing on festival satisfaction among teenage festivalgoers, to derive strategies that festival organizers could employ to maintain long-term relationships with their audience. In this paper, we followed the festival attribute and SNS photo sharing behavior approaches for developing measurement constructs, by investigating previous related studies. Statistical analyses revealed that festival attributes positively impact consumer perceptions of festival value, which in turn influences SNS photo sharing behavior and festival satisfaction. In addition, SNS photo sharing behavior has a significant mediation effect on the process. Moreover, the differing intensities of SNS usage among the festivalgoers resulted in consumers presenting significantly different SNS photo sharing behavior. Our findings offer insights for marketers in the festival business who are interested in gaining a better understanding of the behavior of teenage festivalgoers.

Genshin Impact is an open-world action role-playing game launched by miHoYo in 2020 and has been released on multiple platforms. Due to the excellent character design, it has received widespread attention once it was released, and it has been ranked in the top 10 best-selling lists in several countries after a few weeks of launch. This article will first analyze the map design of the game's open world, and what elements are used on the game map to allow players to immerse themselves in it. Then, in the gameplay of the game, a rich and varied combat system is formed by using the relationship between the elements to restrain each other.There is also a card drawing system added to it, which makes the acquisition of characters full of randomness and allows more players to participate in it. It is concluded that the novel design and rich and varied play methods have made the game Genshin Impact high in popularity, occupying the forefront of the app download list for a long time.

This study explores the role of brand value in the global expansion of Korean food franchise companies, using big data text analysis via the Textom platform. Drawing from unstructured text collected from digital sources, the study conducted word frequency analysis and CONCOR network analysis to reveal key semantic structures in consumer and media discourse. Prominent keywords such as “brand,” “localization,” “global market,” and “Hallyu” emerged as central themes, reflecting the strategic importance of cultural identity and adaptation in overseas expansion. The CONCOR analysis identified dense keyword clusters around “brand trust,” “K-food,” and “consumer recognition,” illustrating how brand value is framed in public discussion. These findings demonstrate that strategic messaging and localized brand communication are essential to franchise success abroad. By combining qualitative nuance with quantitative depth, this study offers actionable insights for marketers and strategists aiming to strengthen brand equity in global markets. Future research should extend to platform-specific sentiment shifts and regional differences in perception.