Sungkyunkwan University · 社会科学
Professor Jinyoung Han's research lab specializes in the intersection of data science, artificial intelligence, and real-world societal and health challenges. The lab focuses on leveraging machine learning and natural language processing to analyze large-scale social media data for understanding human behavior, emotional dynamics during crises like the COVID-19 pandemic, and the spread of misinformation. In healthcare, the lab develops deep learning models—particularly convolutional neural networks—for medical image classification, aiming to improve early diagnosis and classification of retinal diseases such as neovascular age-related macular degeneration and central serous chorioretinopathy. The lab also investigates user behavior in social curation platforms like Pinterest, exploring content sharing, user engagement, and personalization.
Figures are computed from collected data and may differ slightly.
Spreading rumors on the Internet has become increasingly pervasive due to the proliferation of online social media. This paper investigates how rumors are amplified by a group of users who share similar interests or views, dubbed as an echo chamber. To this end, we identify and analyze 'rumor' echo chambers, each of which is a group of users who have participated in propagating common rumors. By collecting and analyzing 125 recent rumors from six popular fact-checking sites, and their associated
Customer retention has been one of the most recognized research issues in the service industry. The next on the list is predicting customer behaviour or understanding customer intent, which is particularly important for the hospitality and tourism industries. This study investigates the customers' hotel revisiting behaviour using a large-scale customer review data, which can shed light on the potentiality of (i) identifying the factors that are associated with the customer revisiting behaviour a
Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and normal healthy patients were analyzed using a convolutional neural network (CNN). The model was trained and validated based on 4749
Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their
The long-term uncertainty and persistence of isolation caused by the COVID-19 pandemic created prolonged emotional distress individually and collectively. As the pandemic progressed, the dynamic ride of emotional experience was expressed live and shared online, particularly on social media. In this study, we collected posted messages on Twitter for a longitudinal investigation to determine how emotional experiences changed over time during the pandemic. In total, we analyzed 41,868,013 COVID-19-
Neovascular age-related macular degeneration (nAMD) and central serous chorioretinopathy (CSC) are two of the most common macular diseases. This study proposes a convolutional neural network (CNN)-based deep learning model for classifying the subtypes of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and CSC (chronic CSC and acute CSC) and healthy individuals using single spectral-domain optical coherence tomography (SD-OCT) images. The proposed mod
We conduct comprehensive measurements on the current practice of content bundling to understand the structural patterns of torrents and the participant behaviors of swarms on one of the largest BitTorrent portals: The Pirate Bay. From the datasets of the 120K torrents and 14.8M peers, we investigate what constitutes torrents and how users participate in swarms from the perspective of bundling, across different content categories: Movie, TV, Porn, Music, Application, Game and E-book. In particula
The word-of-mouth diffusion has been regarded as an important mechanism to advertise a new idea, image, technology, or product in online social networks (OSNs). This paper studies the prediction of popular and viral image diffusion in Pinterest. We first characterize an image cascade from two perspectives: (i) volume — how large the cascade is, that is, total number of users reached, and (ii) structural virality — how many users in the cascade are responsible for attracting other users. Our mode
Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their
Pinterest provides a social curation service where people can collect, organize, and share content (pins in Pinterest) that reflect their interests. This paper investigates (1) the differences in pinning (i.e., the act of posting a pin) and repinning (i.e., the act of sharing other user's pin) behaviors by topics and user gender, and (2) the relations among topics in Pinterest. We conduct a measurement study using a large-scale dataset (1.6 M pins shared by 1.1 M users) in Pinterest. We show tha
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