• Information Systems Review
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• v.18 no.2
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• pp.199-223
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• 2016

As social networking services (SNS) become widely used tools for maintaining social relationships, people use SNS to express themselves online. Users are free to form multiple characters in SNS because of online anonymity. This phenomenon causes SNS users to easily demonstrate multiple personas that are different from their identities in the real world. Therefore, this study focuses on online multi-personas that establish multiple fake identities in the SNS environment. The main objective of this study is to investigate factors that affect online multi-personas. Fake online identities can have various negative consequences such as cyber bullying, cyber vandalism, or antisocial behavior. Since the boundary between the online and offline worlds is fading fast, these negative aspects of online behavior may influence offline behaviors as well. This study focuses on teenagers who often create multi-personas online. According to previous studies, personal identities are usually established during a person's youth. Based on data on 664 teenage users, this study identifies four emotional factors, namely, closeness with others, relative deprivation, peer pressure and social norms. According to data analysis results, three factors (except closeness with others) have positive correlations with users' multi-personas. This study contributes to the literature by identifying the factors that cause young people to form online multi-personas, an issue that has not been fully discussed in previous studies. From a practical perspective, this study provides a basis for a safe online environment by explaining the reasons for creating fake SNS identities.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.726-729
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• 2023

본 논문에서는 대화 중 발화에서 페르소나 트리플을 추출하는 방법을 연구한다. 발화 문장과 그에 해당하는 트리플 쌍을 활용하여 발화 문장 혹은 페르소나 문장이 주어졌을 때 그로부터 페르소나 트리플을 추출하도록 모델을 멀티 태스크 러닝 방식으로 학습시킨다. 모델은 인코더-디코더 구조를 갖는 사전학습 언어모델 BART [1]와 T5 [2]를 활용하며 relation 추출과 tail 추출의 두 가지 태스크를 각각 인코더, 디코더 위에 head를 추가하여 학습한다. Relation 추출은 분류로, tail 추출은 생성 문제로 접근하도록 하여 최종적으로 head, relation, tail의 구조를 갖는 페르소나 트리플을 추출하도록 한다. 실험에서는 BART와 T5를 활용하여 각 태스크에 대해 다른 학습 가중치를 두어 훈련시켰고, 두 모델 모두 relation과 tail을 추출하는 태스크 정확도에 있어서 90% 이상의 높은 점수를 보임을 확인했다.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.45-62
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• 2021

This article examines celeactor as cultural contents focused on the multi-persona in a South Korean reality show program . The celeactor is a form of celebrity and part of a celetoid. Celebrity can be categorized by three forms: ascribed form, achieved form and attributed form. In attributed form, the celetoid suddenly becomes popular through the media. The celeactor is a subcategory of the celetoid. The celeactor is defined by a virtual character that exist in temporary or institutionalized traits of popular culture. The Korean celebrity culture presents Korean intellectual factors, spiritual aspects, tastes, moral virtue, power relationships and traditional hierarchy. In order to examine the features of the Korean celeactor in cultural contents, this article focuses on the multi-persona of celebrity in South Korean popular culture through fantasy, challenge and nostalgia. This article examines the multi-persona of celebrity represented in South Korean popular culture as a negotiated response to cultural identity deconstructed and reconstructed in performance. The research methodology is to analyze a South Korean television reality show program on MBC Hangout with Yoo that represents various sub-characters performed by Jae-suk Yoo, a South Korean comedian and host. As for theoretical underpinning, this exploration joins work on Erving Goffman's (1959) notion of self-presentation and Chris Rojek's (2001) celebrity studies.

• The Research Journal of the Costume Culture
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• v.29 no.4
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• pp.603-615
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• 2021

The aim of this study was to analyze the semantic network structure of keywords and the visual composition of images extracted from Instagram in relation to the multi-persona phenomenon with in fashion imagery, which has recently been attracting attention. To this end, the concept of a 'secondary character', which forms a separate identity from a 'main character' on various social media platforms as well as on the airwaves, was considered as the spread of multi-persona and #SecondaryCharacter on Instagram was investigated. 3,801 keywords were collected after crawling the data using Python and morphological analysis was undertaken using KoNLP. The semantic network structure was then examined by conducting a CONCOR analysis using UCINET and Netdraw to determine the top 50 keywords. The results were then classified into a total of 6 clusters. In accordance with the meaning and context of the keywords included in each cluster, group names were assigned : virtual characters, relationship with the main character, hobbies, daily record, N-job person, media and marketing. Image analysis considered the technical, compositional, and social styles of the media based on Gillian Rose's visual analysis method. The results determined that Instagram uses fashion images that virtualize one's face to produce multi-persona representation s that show various occupations, describe different types of hobbies, and depict situations pertaining to various social roles.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.491-496
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• 2022

최근 멀티-세션 데이터로 장기간 페르소나와 대화 일관성을 유지하며 인터넷에서 대화와 관련된 지식을 활용하는 대화모델 연구가 활발히 진행되고 있다. 하지만 이를 위한 한국어 멀티-세션 오픈 도메인 지식 기반 대화 데이터는 공개되지 않아 한국어 대화모델 연구에 어려움이 있다. 따라서 본 논문에서는 한국어 멀티-세션 오픈 도메인 지식 기반 데이터의 필요성을 시사하고, 데이터 수집을 위한 툴을 제안한다. 제안하는 수집 툴은 양질의 데이터 수집을 위해 작업자들이 사용하기 편하도록 UI/UX를 구성하였으며, 대화 생성 시 텍스트뿐만 아니라 정보가 밀집된 테이블도 대화에 활용할 지식으로 참조할 수 있도록 구현하였다. 제안하는 수집 툴은 웹 랜덤채팅 시스템에 기반을 두어 작업자가 여러 다른 작업자와 같은 확률로 매칭되게 구현되었으며, 일정 확률로 기존 대화로부터 대화를 시작하도록 함으로써 멀티-세션 대화 수집이 가능하도록 하였다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.4
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• pp.753-760
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• 2015

The Korean wine market is growing year by year recently especially in Off-trade market. It shows not just a part of the very few consumer-driven trend. The purchases of wine is always not so easy because the risk factors and failure points occur when buying wines. Most of wine consumers feel difficulties when buying wines at phase of previous step, buying step and after buying step. Wine consumers can be divided into the wine experts, wine lovers, novices, outsiders. To guide an wine buying with less failures to each representative of wine consumer characteristics and needs, user persona and scenario for the reduced failure were derived.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.