Hayun Song
Sungkyunkwan University · 医学
研究室紹介
Professor Hayun Song's research lab specializes in health communication and human-computer interaction, with a focus on the integration of artificial intelligence and digital technologies in healthcare. The lab explores how AI-driven tools—such as conversational agents, wearable devices, and AI-based diagnostic systems—influence user trust, comprehension, and behavior, particularly among older adults and healthcare professionals. Key research directions include the design of socially assistive robots and virtual agents, the impact of persona and communication strategies in AI health systems, and the role of self-related cues in mitigating digital media overuse. The lab also investigates emotional content in digital health communication and its effects on user engagement and decision-making.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
14현대기술의 발전과 고령화 추세의 가속, 예방 중심의 헬스 케어 패러다임 변화는 헬스 커뮤니케이션과 헬스 커뮤니케이션학에 다양한 변화를 일으키고 있다. 이 글에서는 인공지능을 중심으로 헬스 케어와 헬스 커뮤니케이션 분야에 있어서의 함의를 1) 건강정보의 수집 및 분석과 2) 건강정보의 소통을 중심으로 살펴보았다. 구체적으로는, 개인에 대한 데이터를수집 및 분석할 수 있는 웨어러블 기기와 모바일 기기의 사용, 분석한 데이터를 사용자에게 잘 전달하기 위한 데이터 기반의 환자-의사 커뮤니케이션, 그리고 사회적 보조 로봇(SAR)과 가상 에이전트 등의 사회적 상호작용이 가능한 인공지능과의 커뮤니케이션에 대해 소개하였다. 더불어, 앞으로 헬스 커뮤니케이션과 인공지능 연구에서 잠재적으로 중요한 이슈로서 인공지능의 책임과 설명 가능성, 신뢰, 커뮤니케이션 방식, 그리고 인공지능의 정보 전달 방법에 대한 이슈에 대하여 논의하였다. 마지막으로 앞으로의 헬스 커뮤니케이션과 인공지능 연구에 있어 잠재적
이미지 공유 기반의 인스타그램은 패션 브랜드의 효과적인 마케팅 수단으로 주목 받고 있다. 인스타그램에서는 기존의 광고문법을 탈피한 광고 이미지들이 많이 사용되는 경향이 나타나는데, 이러한 이미지들의 특징을 실질적으로 살펴본 연구는 매우 드물다. 이에 본 연구는 설득지식모델을 바탕으로 인스타그램에서 나타나는 글로벌 패션 브랜드의 전반적인 광고 이미지 특성을 또 텍스트 기반의 소셜 미디어인 페이스북과 비교하여 분석한다. 인스타그램과 페이스북의 광고 이미지가 광고의도와 판매목적을 드러내는 이미지 특성들(e.g., 광고성 메시지 유무, 로고 및 제품의 노출 유무, 로고의 가시성)을 어떻게 활용하고 있는지 비교하여 살펴보고, 각각의 특성들이 사용자 참여도와는 어떤 상관관계를 갖는지를 알아본다. 분석을 위해, 인스타그램과 페이스북에 게시된 10개 글로벌 패션 브랜드의 게시글 2,000개를 수집한 후, 각 미디어와 브랜드 유형에 따른 이미지 특성들에 대해 내용분석을 실시하였다. 연구 결과, 인스
The widespread, addictive consumption of short-form videos, which allegedly causes “brain rot,” has become an urgent public concern. This study proposes that self-related cues serve as an intrinsic, self-reflective strategy that enhances self-control over media overuse. We developed an app that de-immerses users by periodically displaying different self-related cues (live camera, selfie, name in text, and black screen) and tested their effects in a laboratory experiment (N=84). Overall, findings
AI-based computer-aided diagnosis (AI-CAD) systems are transforming medical imaging by augmenting clinicians in disease identification and diagnosis. Nonetheless, little is known about how individual differences, particularly clinicians’ expertise, affect their perception, trust, and adoption of such systems. Guided by the Elaborated Likelihood Model (ELM), this study systematically compared Task Experts (TEs; retina specialists; n = 38) and Task Non-Experts (TNs; general ophthalmologists; n = 2
AI-based computer-aided diagnosis (AI-CAD) systems are transforming medical imaging by augmenting clinicians in disease identification and diagnosis. Nonetheless, little is known about how individual differences, particularly clinicians’ expertise, affect their perception, trust, and adoption of such systems. Guided by the Elaborated Likelihood Model (ELM), this study systematically compared Task Experts (TEs; retina specialists; <i>n</i> = 38) and Task Non-Experts (TNs; general ophthalmologists
AI-based computer-aided diagnosis (AI-CAD) systems are transforming medical imaging by augmenting clinicians in disease identification and diagnosis. Nonetheless, little is known about how individual differences, particularly clinicians’ expertise, affect their perception, trust, and adoption of such systems. Guided by the Elaborated Likelihood Model (ELM), this study systematically compared Task Experts (TEs; retina specialists; <i>n</i> = 38) and Task Non-Experts (TNs; general ophthalmologists
AI conversations are often perceived as anonymous and private spaces that encourage candid self-expression, yet they can also increase vulnerability to offensive and toxic language use. This may be amplified in voice-based interactions, reducing users’ awareness of their own language behavior. While research has addressed offensive language detection and AI responses, little is known about whether such approaches actually influence users’ language use or self-awareness. We conducted semi-structu
Despite the growing importance of video-based social media content, such as vlogs, as a marketing tool in the travel industry, there is limited research on the characteristics that enhance engagement among potential travelers. This study explores the influence of emotional valence in YouTube travel content on viewer engagement, specifically likes and comments. We analyzed 4,619 travel-related YouTube videos from eight popular tourist cities. Using negative binomial regression analysis, we found
This study investigates instructional methods to support older adults in independently using digital healthcare applications for dementia prevention. Grounded in social cognitive theory, the research evaluates the effects of virtual agent demonstrations and “split persona” roles. By employing a 2 (demonstration: demonstration vs. no demonstration) x 2 (split persona: one vs. two) between-subjects design, the study examines the effects on elderly participants’ task comprehension and their attitud
As the adoption of digital technologies accelerates, conversational agents are increasingly employed in healthcare. The personas of these agents play an important role in user interaction. This study explores the design and implementation of conversational agents in healthcare, specifically aiming to examine the effects of split persona where agents are distinguished by their roles. An experiment (N = 84 participants) was conducted using a 2 (nurse agent: absence vs. presence) × 2 (doctor agent: